Impact of Scottish smoke-free legislation on smoking quit attempts and prevalence

<p><b>Objectives:</b> In Scotland, legislation was implemented in March 2006 prohibiting smoking in all wholly or partially enclosed public spaces. We investigated the impact on attempts to quit smoking and smoking prevalence.</p> <p><b>Methods:</b> We performed time series models using Box-Jenkins autoregressive integrated moving averages (ARIMA) on monthly data on the gross ingredient cost of all nicotine replacement therapy (NRT) prescribed in Scotland in 2003–2009, and quarterly data on self-reported smoking prevalence between January 1999 and September 2010 from the Scottish Household Survey.</p> <p><b>Results:</b> NRT prescription costs were significantly higher than expected over the three months prior to implementation of the legislation. Prescription costs peaked at £1.3 million in March 2006; £292,005.9 (95% CI £260,402.3, £323,609, p<0.001) higher than the monthly norm. Following implementation of the legislation, costs fell exponentially by around 26% per month (95% CI 17%, 35%, p<0.001). Twelve months following implementation, the costs were not significantly different to monthly norms. Smoking prevalence fell by 8.0% overall, from 31.3% in January 1999 to 23.7% in July–September 2010. In the quarter prior to implementation of the legislation, smoking prevalence fell by 1.7% (95% CI 2.4%, 1.0%, p<0.001) more than expected from the underlying trend.</p> <p><b>Conclusions:</b> Quit attempts increased in the three months leading up to Scotland's smoke-free legislation, resulting in a fall in smoking prevalence. However, neither has been sustained suggesting the need for additional tobacco control measures and ongoing support.</p&gt

Design of Experiments for Screening

The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs and systematic fractional replicate designs. Generic issues of aliasing, bias and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are discussed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modelling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Balance algorithm for cluster randomized trials

<p>Abstract</p> <p>Background</p> <p>Within cluster randomized trials no algorithms exist to generate a full enumeration of a block randomization, balancing for covariates across treatment arms. Furthermore, often for practical reasons multiple blocks are required to fully randomize a study, which may not have been well balanced within blocks.</p> <p>Results</p> <p>We present a convenient and easy to use randomization tool to undertake allocation concealed block randomization. Our algorithm highlights allocations that minimize imbalance between treatment groups across multiple baseline covariates.</p> <p>We demonstrate the algorithm using a cluster randomized trial in primary care (the PRE-EMPT Study) and show that the software incorporates a trade off between independent random allocations that were likely to be imbalanced, and predictable deterministic approaches that would minimise imbalance. We extend the methodology of single block randomization to allocate to multiple blocks conditioning on previous allocations.</p> <p>Conclusion</p> <p>The algorithm is included as Additional file <supplr sid="S1">1</supplr> and we advocate its use for robust randomization within cluster randomized trials.</p> <suppl id="S1"> <title> <p>Additional File 1</p> </title> <text> <p><b>Cluster randomization allocation algorithm version 1.</b> Algorithms scripted in R to provide robust cluster randomization.</p> </text> <file name="1471-2288-8-65-S1.zip"> <p>Click here for file</p> </file> </suppl

Whatever the Weather: Ambient Temperature Does Not Influence the Proportion of Males Born in New Zealand

BACKGROUND: The proportion of male births has been shown to be over 50% in temperate climates around the world. Given that fluctuations in ambient temperature have previously been shown to affect sex allocation in humans, we examined the hypothesis that ambient temperature predicts fluctuations in the proportion of male births in New Zealand. METHODOLOGY/PRINCIPAL FINDINGS: We tested three main hypotheses using time series analyses. Firstly, we used historical annual data in New Zealand spanning 1876-2009 to test for a positive effect of ambient temperature on the proportion of male births. The proportion of males born ranged by 3.17%, from 0.504 to 0.520, but no significant relationship was observed between male birth rates and mean annual temperature in the concurrent or previous years. Secondly, we examined whether changes in annual ambient temperature were negatively related to the proportion of male stillbirths from 1929-2009 and whether the proportion of male stillbirths negatively affected the proportion of male live births. We found no evidence that fewer male stillbirths occurred during warmer concurrent or previous years, though a declining trend in the proportion of male stillbirths was observed throughout the data. Thirdly, we tested whether seasonal ambient temperatures, or deviations from those seasonal patterns, were positively related to the proportion of male births using monthly data from 1980-2009. Patterns of male and female births are seasonal, but very similar throughout the year, resulting in a non-seasonal proportion of male births. However, no cross correlations between proportion of male births and lags of temperature were significant. CONCLUSIONS: Results showed, across all hypotheses under examination, that ambient temperatures were not related to the proportion of male births or the proportion of male stillbirths in New Zealand. While there is evidence that temperature may influence human sex allocation elsewhere, such effects of temperature are not universal

'The mum has to live with the decision much more than the dad'; a qualitative study of men's perceptions of their influence on breastfeeding decision-making

© 2018 The Author(s). Background: Although breastfeeding is widely acknowledged as the normal method of infant feeding, there are large variations in rates of initiation and duration. Several factors are linked to the likelihood of breastfeeding initiation, including the influence and opinion of the child's father. There is limited research into men's perception of their influence, or if they feel it appropriate to be involved in deciding how to feed their children. The aim of this study was to investigate, using a qualitative methodology, fathers' perceptions of their influence on the decision to feed their child breastmilk or formula. Methods: Six men were recruited through Children's Centres in Bristol, United Kingdom, and a phenomenological research methodology implemented using semi-structured interviews. Specific objectives were: to understand participants' views on breastfeeding; understand if and how these views were discussed with their partner; to determine if participants believed involvement in the feeding decision is appropriate; to understand how they felt about the decision made; and to see if their views changed after the birth of their child. Results: Multiple themes emerged during analysis, including deferring of responsibility to the mother; breastfeeding as normal practice; change in attitude; involvement in parenting; and, advantages for the father. The men in the study accepted breastfeeding as normal behaviour, probably because of their upbringing in households where breastfeeding was practiced. There was consensus that women had more say in deciding to breastfeed, which was explained as a consequence of their greater involvement. It could also be interpreted as an unwillingness to interfere in an area perceived as 'owned' by women. Participants acknowledged that breastfeeding was more difficult than they had perceived. Conclusions: The key themes emerging from the interviews are suggestive of an impact on breastfeeding interventions that use the father as an intermediary. If they do not feel that they are 'permitted' to comment on their partner's breastfeeding, then simply increasing knowledge of breastfeeding benefits in these men is likely to have minimal impact

Achieving In Vivo Target Depletion through the Discovery and Optimization of Benzimidazolone BCL6 Degraders.

Deregulation of the transcriptional repressor BCL6 enables tumorigenesis of germinal center B-cells, and hence BCL6 has been proposed as a therapeutic target for the treatment of diffuse large B-cell lymphoma (DLBCL). Herein we report the discovery of a series of benzimidazolone inhibitors of the protein-protein interaction between BCL6 and its co-repressors. A subset of these inhibitors were found to cause rapid degradation of BCL6, and optimization of pharmacokinetic properties led to the discovery of 5-((5-chloro-2-((3R,5S)-4,4-difluoro-3,5-dimethylpiperidin-1-yl)pyrimidin-4-yl)amino)-3-(3-hydroxy-3-methylbutyl)-1-methyl-1,3-dihydro-2H-benzo[d]imidazol-2-one (CCT369260), which reduces BCL6 levels in a lymphoma xenograft mouse model following oral dosing

Potential for early warning of viral influenza activity in the community by monitoring clinical diagnoses of influenza in hospital emergency departments

<p>Abstract</p> <p>Background</p> <p>Although syndromic surveillance systems are gaining acceptance as useful tools in public health, doubts remain about whether the anticipated early warning benefits exist. Many assessments of this question do not adequately account for the confounding effects of autocorrelation and trend when comparing surveillance time series and few compare the syndromic data stream against a continuous laboratory-based standard. We used time series methods to assess whether monitoring of daily counts of Emergency Department (ED) visits assigned a clinical diagnosis of influenza could offer earlier warning of increased incidence of viral influenza in the population compared with surveillance of daily counts of positive influenza test results from laboratories.</p> <p>Methods</p> <p>For the five-year period 2001 to 2005, time series were assembled of ED visits assigned a provisional ED diagnosis of influenza and of laboratory-confirmed influenza cases in New South Wales (NSW), Australia. Poisson regression models were fitted to both time series to minimise the confounding effects of trend and autocorrelation and to control for other calendar influences. To assess the relative timeliness of the two series, cross-correlation analysis was performed on the model residuals. Modelling and cross-correlation analysis were repeated for each individual year.</p> <p>Results</p> <p>Using the full five-year time series, short-term changes in the ED time series were estimated to precede changes in the laboratory series by three days. For individual years, the estimate was between three and 18 days. The time advantage estimated for the individual years 2003–2005 was consistently between three and four days.</p> <p>Conclusion</p> <p>Monitoring time series of ED visits clinically diagnosed with influenza could potentially provide three days early warning compared with surveillance of laboratory-confirmed influenza. When current laboratory processing and reporting delays are taken into account this time advantage is even greater.</p

Optimal fetal growth for the Caucasian singleton and assessment of appropriateness of fetal growth: an analysis of a total population perinatal database

BACKGROUND: The appropriateness of an individual's intra uterine growth is now considered an important determinant of both short and long term outcomes, yet currently used measures have several shortcomings. This study demonstrates a method of assessing appropriateness of intrauterine growth based on the estimation of each individual's optimal newborn dimensions from routinely available perinatal data. Appropriateness of growth can then be inferred from the ratio of the value of the observed dimension to that of the optimal dimension. METHODS: Fractional polynomial regression models including terms for non-pathological determinants of fetal size (gestational duration, fetal gender and maternal height, age and parity) were used to predict birth weight, birth length and head circumference from a population without any major risk factors for sub-optimal intra-uterine growth. This population was selected from a total population of all singleton, Caucasian births in Western Australia 1998–2002. Births were excluded if the pregnancy was exposed to factors known to influence fetal growth pathologically. The values predicted by these models were treated as the optimal values, given infant gender, gestational age, maternal height, parity, and age. RESULTS: The selected sample (N = 62,746) comprised 60.5% of the total Caucasian singleton birth cohort. Equations are presented that predict optimal birth weight, birth length and head circumference given gestational duration, fetal gender, maternal height, age and parity. The best fitting models explained 40.5% of variance for birth weight, 32.2% for birth length, and 25.2% for head circumference at birth. CONCLUSION: Proportion of optimal birth weight (length or head circumference) provides a method of assessing appropriateness of intrauterine growth that is less dependent on the health of the reference population or the quality of their morphometric data than is percentile position on a birth weight distribution