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

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

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

Comparison of four analytic strategies for complex survey data: a case-study of Spanish data

Purpose: The aim of this secondary data analysis was to investigate the effect of four different analytical strategies: Model Based Analysis (MBA), Design Based Analysis (DBA), Multilevel Model Based Analysis (MMBA), and Multilevel Design Based Analysis (MDBA), on the model estimates for complex survey data. Methods: Using data from the World Health Survey-Spain explanatory models for the outcome Metabolic Equivalent of Task (METs) were calculated using MBA, DBA, MMBA, and MDBA. Regression coefficients, standard errors (SE) and the Akaike Information Criterion (AIC) from all the models were compared. Results: DBA showed highest estimates for most of the variables, including consistently higher SE than all other model - 20% to 48% higher than estimates for MBA, 10% to 37% for MMBA and 23% to 35% for MDBA. The SE for MDBA were 2.5% to 13% higher than estimates derived from MMBA in level 1 predictors, but SE in MMBA was higher by 18% for level 2 predictors. Values of AIC suggested the model derived by MDBA was the best fit and DBA the poorest fit of the four models. Conclusion: With minimum AIC, MDBA appeared to be the most appropriate approach to analyze complex survey data. To confirm the finding of present study a future work on a simulation data would be required