Fetal surgery has become a clinical reality, with interventions for twin‐to‐twin transfusion syndrome (TTTS) and spina bifida demonstrated to improve outcome. Fetal imaging is evolving, with the use of 3D ultrasound and fetal MRI becoming more common in clinical practise. Medical imaging analysis is also changing, with technology being developed to assist surgeons by creating 3D virtual models that improve understanding of complex anatomy, and prove powerful tools in surgical planning and intraoperative guidance.We introduce the concept of computer‐assisted surgical planning, and present the results of a systematic review of image reconstruction for fetal surgical planning that identified six articles using such technology.Indications from other specialities suggest a benefit of surgical planning and guidance to improve outcomes. There is therefore an urgent need to develop fetal‐specific technology in order to improve fetal surgical outcome. © 2015 The Authors. Prenatal Diagnosis published by John Wiley & Sons Ltd. What's already known about this topic?Fetal surgery has now become a clinical reality, with interventions such as laser treatment for twin‐to‐twin transfusion syndrome (TTTS) and open fetal surgery for spina bifida demonstrated in randomised control trials to improve neonatal outcomeOther specialities are increasingly utilising computer‐assisted surgical planning software, with evidence that this can improve outcomeWhat does this study add?We feel that there is an urgent need to develop fetal‐specific technology for surgical planning as it is likely to play an important role in improving outcomes from fetal surgery Imaging of the fetus using ultrasound, and increasingly magnetic resonance imaging (MRI), provides ever‐increasing diagnostic and prognostic information, which guides parental counselling and consideration of and decision making about mode and time of delivery. Fetal surgery has now become a clinical reality,In this review we first briefly present the processes involved in surgical planning and the technology being developed to manipulate fetal imaging and reconstruct 3D virtual fetal models for surgical planning. The results of a systematic review on the use of fetal image reconstruction to aid surgical planning are then presented. We finally consider the current state of surgical planning in other specialities, and how challenges faced in fetal surgery may benefit from surgical planning.For the purpose of this review surgical planning refers to computer‐assisted preoperative modelling and visualisation of anatomy in order to predefine the surgical steps, determine the best plan and transfer it to reality for an individual patient. Figure The first stage in surgical planning is to take high quality images of the structures of interest. The images are then manipulated using specifically developed software to perform surface and volume rendering. Different imaging modalities can be fused to gain additional anatomical information.Finally the 3D model can be used in association with instrument tracking systems to provide guidance intra‐operatively. Augmented reality aligns preoperative spatially accurate segmented medical imaging over real‐time laparoscopic views, or imaging can be visualised in parallel on additional screens. This demonstrates the position of surgical instrument in relation to sub‐surface structures to the surgeon, and helps align and guide them into the optimal position reaching the area of interest in the best possible orientation, and thus improving outcome and reducing complication rate.3D and 4D ultrasound is now becoming more widely available within the clinical setting, and has notably been found to be useful in the diagnosis of facial, neural tube and skeletal abnormalities.Magnetic resonance imaging (MRI) is being increasingly employed as an adjunct to ultrasound imaging, and is generally accepted to be safe.As the fetus is not sedated, a major difficulty with fetal MRI is movement artefact. For this reason instead of 3D volumetric imaging, which takes time to acquire, snapshot imaging techniques are utilised, such as half‐Fourier turbo spin echo (HASTE) and single shot fast spin echo (SSFSE). These produce a stack of motion‐frozen 2D slices with an in‐plane spatial resolution of around 1 mm, allowing clinical interpretation.For imaging to be useful for computer‐assisted surgical planning 3D volumes are required. Reconstructing 3D fetal images from 2D MRI slices is challenging. Difficulties include low image resolution, low signal‐to‐noise contrast, maternal and fetal movement that is unpredictable and can be large and the rapid development of fetal organs with gestation causing the shape and size of the structure of interest to change significantly.In order to create a 3D model from 2D slices fetal movement needs to be corrected for. Slices are usually acquired in an interleaved manner so as to reduce scan time whilst avoiding slice cross‐talk artefacts.Various approaches have been developed to tackle the difficulties of autocorrecting motion corrupted slices, as summarised in Table Prior to reconstruction, the tissue of interest needs to be identified, excluding all other fetal and maternal tissue information that would interfere with the algorithms used in 3D reconstruction. For clinical application, this identification, a form of segmentation, needs to be automatic, or semi‐automatic. Ideally the ‘recognition’ of fetal structures should involve minimal user input, reducing the time and financial costs for clinicians, and also preventing inter‐observer variability and therefore standardising the results.Many automated segmentation techniques focus on the process of comparing images to a pre‐defined gestation specific reference atlas to identify structures of interest.Most 3D reconstruction and segmentation work has been performed on fetal brain, a relatively rigid structure with minimal motion outside that of head movement. However, the techniques developed could be translated to reconstruct and segment other fetal structures, and have been used to reconstruct the fetal thorax and perform automated lung segmentation, demonstrating accurate fetal lung volume calculation from 20 to 38 week gestation.We are interested in how this developing field of technology can be utilised in relation to fetal surgery, with a goal of improving outcome.We performed a systematic review on the use of image reconstruction in fetal surgical planning to see how surgical planning tools have been employed in clinical practise.To search the literature MeSH terms and keywords relating to the image reconstruction  were combined with terms for fetal surgery . PUBMED and EMBASE were searched electronically on 13 April 2015. Reference lists of relevant articles and reviews were hand searched by RP for additional literature, and the articles that cited them reviewed. Titles and abstracts were screened for relevance by RP. Full text copies of relevant articles were retrieved and read in full by RP who agreed the final list with ALD.Studies published at any time that discussed the use of image reconstruction specifically for surgical planning, and not for diagnostic or prognostic reasons alone, were included. Participants were pregnant women with normal or complicated single or multiple pregnancies. Animal studies were excluded.A total of 248 studies were identified, of which 215 were excluded after review of the title and abstract. The remaining 33 were read in full, and 26 excluded for not relating to surgical planning, or not involving image reconstruction or 3D imaging. The six included studies describe cases in which surgical planning tools were used for treatment of fetal structural abnormality such as airway obstruction, facial cleft and spina bifida, or twin complications . Virtual bronchoscopy was successful in all cases, demonstrating no tracheal invasion and patent airways in all cases. EXIT procedure was therefore avoided, and all babies were delivered by caesarean section with good postnatal outcome.Two groups have gone further in using computer reconstructions to assist management decisions. Werner et al.Luks This systematic review of fetal surgical planning identified six relevant studies in which surgical planning tools were used for treatment of fetal structural abnormality or twin complications. Subjectively the application of medical image computing improved pre‐operative planning, but it was not possible to identify if there was an impact on fetal, maternal or neonatal outcome, because of the small number of studies.Minimally invasive endoscopic abdominal surgery is a broadly similar surgical technique to fetoscopic surgery, in which surgical planning software is being increasingly investigated in laparoscopic renal, liver and pancreatic surgery, with some evidence of improved outcome. Preoperative planning and real‐time assisted navigation using 3D reconstructions in laparoscopic partial nephrectomy have been shown to reduce operation time and blood loss when compared to controls,Surgical planning and intraoperative guidance are also used extensively in cranial surgery, ear, nose and throat surgery, and orthopaedics. Examples of different computer‐assisted planning and intraoperative tools available commercially in different specialities are given in Table In order to be of use for surgical planning 3D reconstruction needs to clearly demonstrate complex anatomy. Fetal MRI imaging is improving, with higher resolution and improved signal‐to‐noise ratio. The computer processing of this data to produce 3D reconstruction with segmentation is also developing, and so it seems timely that these technologies are integrated into a surgical planning platform. Such technology could help plan port placement for fetal and placental surgery and improve anatomical understanding through virtual bronchoscopy or cystoscopy.Fusion of different imaging modalities, for example MRI for greater soft tissue anatomic detail with Ultrasound Doppler to demonstrate vasculature, could help further improve anatomical understanding. Fusion of fetoscopic images with preoperative imaging has already been developed, with work aimed at mosaicking endoscopic placental images onto 3D ultrasound placental images. This would allow surgeons performing laser treatment to visualise the entire placental anatomy, and not just the minimal field of view available through the scope.Instrument tracking could also help surgeons perform more complex percutaneous procedures, demonstrating device position on 3D imaging and so helping to locate more precisely fetal target organs or vessels. As the boundaries of fetal intervention expand with new potential treatments, such as fetal pace makers3D models may also have an important role in preoperative patient counselling, in order to improve the patients' understanding of the pathology, the planned operation, and to prepare patients for delivery of a baby with anatomical abnormalities. The increasing availability of high‐quality 3D printers at reasonable cost may further help prepare patients by providing models they can see, hold and even show to friends and family.New technologies are also being investigated to assist surgical training, particularly in specialities where surgery is complex, and where the pathology is rare, reducing exposure within clinical practise. Efforts have been made to produce high‐fidelity simulators for fetal interventions.Fetal surgery has now become a clinical reality, with interventions such as laser treatment for twin‐to‐twin transfusion syndrome (TTTS) and open fetal surgery for spina bifida demonstrated in randomised control trials to improve neonatal outcomeOther specialities are increasingly utilising computer‐assisted surgical planning software, with evidence that this can improve outcomeWe feel that there is an urgent need to develop fetal‐specific technology for surgical planning as it is likely to play an important role in improving outcomes from fetal surgeryThere is some early subjective evidence that 3D reconstruction of fetal imaging is beneficial for fetal surgical planning, but as of yet there are no studies on intraoperative guidance or studies using objective measures of outcome. Fetal surgery is increasingly being offered for the correction of structural malformations and newer fetal therapies are being considered for genetic disease. Indications from other surgical specialities suggest a benefit of computer‐assisted planning and guidance to improve outcomes. Given the constraints of operating in the intrauterine environment and the intricacy of the fetal anatomy, surgical planning tools are likely to play an important role in improving outcomes from fetal surgery, and there is therefore an urgent need to develop fetal‐specific technology for this purpose.


Functional connectivity is the statistical association of neuronal activity time courses across distinct brain regions, supporting specific cognitive processes. This coordination of activity is likely to be highly important for complex aspects of cognition, such as the communication of fluctuating task goals from higher‐order control regions to lower‐order, functionally specific regions. Some of these functional connections are identifiable even when relevant cognitive tasks are not being performed (i.e. at rest). We used magnetoencephalographic recordings projected into source space to demonstrate that resting state networks in childhood have electrophysiological underpinnings that are evident in the spontaneous fluctuations of oscillatory brain activity. Using the temporal structure of these oscillatory patterns we were able to identify a number of functional resting state networks analogous to those reported in the adult literature. In a second analysis we fused this dynamic temporal information with the spatial information from a functional magnetic resonance imaging analysis of functional connectivity, to demonstrate that inter‐subject variability in these electrophysiological measures of functional connectivity is correlated with individual differences in cognitive ability: the strength of connectivity between a fronto‐parietal network and lower‐level processing areas in inferior temporal cortex was associated with spatial working memory capacity, as measured outside the scanner with educationally relevant standardized assessments. This study represents the first exploration of the electrophysiological mechanisms underpinning resting state functional connectivity in source space in childhood, and the extent to which the strength of particular connections is associated with cognitive ability. Magnetoencephalography (MEG) can be used to extract neurophysiological data from typically developing children, which can be used to explore functional connectivity at rest.At rest the temporal structure of neural oscillations can be used to decompose the developing brain into constituent networks.The temporally precise information in MEG can be fused with the spatially precise information in fMRI to explore electrophysiological connections with networks of interest.Across children, connections with a bilateral fronto‐parietal network at rest covary with the child's spatial working memory capacity as measured outside the scanner.The development of methods capable of exploring neural activity at a systems level has opened a number of new research avenues, allowing researchers to go beyond defining the functions of single areas to investigate how and when activity is coordinated across brain areas. One such method is functional connectivity analysis, which includes investigating the temporal correlation of endogenously fluctuating activity between spatially discrete neuronal populations , which measures down‐stream haemodynamic processes occurring in response to changes in neural metabolism. The RSNs observed in children using this technique . Like electroencephalography (EEG), which measures electrical currents conducted to the scalp and commonly used with developmental populations, MEG also noninvasively measures electrical activity. However, it does this by detecting magnetic fields produced by underlying electrical activity, which are not distorted to nearly the same degree by the scalp and skull. As a result, using source reconstruction techniques it is possible to transform the MEG sensor recordings into an estimate of the electrical activity of sources within the brain more reliably than with EEG recordings. Via source reconstruction, MEG therefore offers possibilities for exploring functional connectivity at a neurophysiological and anatomical level in source space. MEG offers comparable temporal resolution to EEG, imaging functional activity on millisecond timescales, and provides information about the frequency content of the neuronal oscillatory patterns. Therefore, unlike fMRI, MEG offers the ability to constrain connectivity analyses to particular frequencies of oscillation (the approach taken here), and to study rapidly changing or transient network structures  methods. Evidence with this technique suggests that in adulthood particular functional connections at rest are related to cognitive ability  the electrophysiological nature of functional brain connectivity in childhood, and (b) relate these new measures of functional connectivity to measures of STM and WM taken outside the scanner. To do this, first we tested whether it is possible to observe RSNs using the temporal structure of spontaneous patterns of oscillatory brain activity within the MEG signal, as measured in children at rest, as has recently been demonstrated in adults  were recruited via local schools. We selected this age range as it is a phase during which complex span WM assessment scores are reliable, but cognitive abilities are still emerging and have yet to reach adult potential . These standard scores were used to characterize our sample (see Results section). However, in our MEG analysis, we used raw scores in the GLM. This is important because the standardized scores from the AWMA are age standardized in whole‐year sections, meaning that children with the same raw score, but who are only a few days apart in age, could have very different age‐standardized scores. The age standardization also alters the distribution of the scores. Instead we used the raw scores in our GLM, but included a separate regressor of age in months. We could then explore the effects of memory capacity while controlling for effects of age. In our analysis we used two composite memory scores, one verbal and one spatial (see Results section). We will subsequently refer to these two measures as verbal WM and spatial WM, respectively, although it is important to note that each contains both one STM and one WM span measure.We conducted an assessment of each child's STM and WM using a number of subtests from the Automated Working Memory Assessment , containing a magnetometer and two orthogonal planar gradiometers at 102 positions , housed in a magnetically shielded room. Data were sampled at 1 kHz, and signals slower than 0.01 Hz were filtered out. A 3D digitizer  was used to record the positions of five head position indicator (HPI) coils and 50–100 additional points evenly distributed over the scalp, all relative to the nasion and left and right preauricular points. We also attached an electrode to each wrist to measure the pulse, and we attached bipolar electrodes to obtain horizontal and vertical electrooculograms (HEOG and VEOG). Head position was monitored throughout the recoding using the HPI coils. Particularly small children were seated on a booster seat to ensure that their head was optimally positioned within the scanner helmet. For the resting state data acquisition, children were instructed to close their eyes, let their mind wander and not think of anything in particular for the duration of the scan. Data acquisition lasted 9 minutes. All children were monitored by video camera throughout the scan, and no child reported having fallen asleep during the scan.r = 0.1 with any of these were subsequently removed from the data. Components dominated by 50 Hz noise were removed to reduce the impact of interference from mains electricity.External noise was removed from the MEG data using the signal space separation method, and adjustments in head position within the recording were compensated for using the MaxMove software, both implemented in MaxFilter version 2.1 (Elekta Neuromag). The MaxFilter software works by mathematically transforming the data to a set of virtual sensors. This is possible because the software has very accurate location measurements of both the MEG sensor array and the subject's head position. Because of our continuous recording of each child's head position, we could check at each sample of the MEG recording that the child's head was accurately transformed to the standard reference frame – thereby controlling for any within‐session head movements. Transforming the data to a set of virtual sensors also allows for the removal of noise emanating from outside the scanner. This signal space separation method acts to suppress any activity that does not stem from virtual channels within the helmet. The data were also down‐sampled to 250 Hz at this stage. The continuous data were visually inspected and any short sections with large signal jumps were removed. This is important, because if left in the data these large signal jumps could have a detrimental influence on the time‐frequency decomposition. A sensor‐space temporal independent component analysis (ICA) was then used to remove artefacts arising from blinks, saccades and pulse‐related cardiac artefacts using a combination of metrics and manual inspection. The temporal ICA was conducted separately for each subject using fastICA run on the sensor space data, and then the time course of each independent component (IC) was correlated with the time course of the VEOG, HEOG and cardiac channels, respectively. Components correlating more than Pearson http://www.fil.ion.ucl.ac.uk/spm/). Prior to beamforming the data were bandpass‐filtered to focus only on the slower frequencies ; previous work has shown that these slower frequencies are better for exploring functional connections with MEG, and increase discrimination between spurious and genuine connectivity , and then divided into 25 temporally independent time series  analysis . Once we had identified the size of these clusters we conducted a sign‐flipping permutation procedure to produce a null distribution, using 5000 permutations. We were then able compare the size of each result to this null distribution thereby identifying the relative alpha level and producing a p‐value. This non‐parametric permutation approach has a number of advantages relative to more traditional approaches to significance testing with electrophysiological data: first, it makes no a priori assumptions about when or where effects are likely to be apparent; second, this approach accounts for multiple comparisons over space and time, which can result in an uncontrolled false positive rate if uncorrected ; Verbal WM – 104.9 (SD = 16.4); Spatial STM – 105.5 (SD = 17.3); Spatial WM – 111.6 (SD = 14.9). We subsequently averaged the two spatial and two verbal measures because they were highly correlated across the 31 children . We used a dependent‐samples Fisher r‐to‐z transform to test whether these two correlations differed significantly, and they did not . These composite verbal and spatial memory scores were later used to explore the relationship between functional connectivity and WM capacity.Our sample of children had the following standardized mean scores: Verbal STM – 113.5 . These were then converted into spatial maps, as described in the Methods Section, and a number of these matched closely with typically reported RSNs identified in MEG and fMRI in adults (Brookes pperm < .05).We also tested the significance of our correlations with a second non‐parametric permutation approach, which controlled for the effect of choosing the best match over 75 maps. In each case we selected the best matching MEG map, and randomly shuffled the allocation of voxel‐wise values across the brain. The process was repeated 75 times, in each case spatial smoothing was applied (5 mm FWHM), to produce a set of 75 random MEG maps. The spatial cross‐correlation between each of these and the candidate fc‐fMRI map was then calculated and the map with the strongest match chosen – just as we had with our real MEG maps. This process was repeated 5000 times in order to produce a null distribution, which characterized the strength of spatial cross‐correlation that would be expected when having 75 random maps and selecting the best match. We could then compare the spatial cross‐correlation from our real map to this distribution to test whether the strength of association is greater than we would have expected by chance  , an early visual component (theta band)  and a sensorimotor component (beta band) . Figure et al., et al., et al., Following this approach, in the beta band we were able to identify a component with separate nodes in the right hemisphere frontal and parietal cortex [et al., pcorrected = .0280). This can be seen in Figure In addition to exploring the presence of RSNs using the temporal ICA, without any spatial constraint, we also defined three a priori seed networks of interest for a comparison across individuals. These were three fronto‐parietal networks derived using a spatial ICA in fMRI data ). These areas are typically associated with both covert and overt spatial attention processes (e.g. Schwartz, Vuilleumier, Hutton, Maravita, Dolan et al., It is noteworthy that we could not identify a similar intrinsic connection predictive of children's verbal WM. There are several possible reasons for this. First, there could be less consistency in the mechanisms children use to solve verbal WM tasks. For example, some children may rely simply upon their basic capacity for retaining verbal material, whereas others may rely upon chunking strategies that lend themselves well to retaining verbal material in serial order (Burgess & Hitch In summary, the current study used the richly informative electrophysiological information obtained through MEG to characterize network connectivity at rest in children, and demonstrated that physiological differences in communication between discrete cortical areas and a fronto‐parietal network at rest are significantly related to individual differences in spatial WM.


CCIS) is a widely accepted measure for risk adjustment in administrative claims data sets. This study assesses the predictive value of CHADS2 scores and CCIS for stroke among patients with prostate cancer. The study was conducted based on data taken from Taiwan's National Health Insurance Research Database (NHIRD). We identified a total of 5414 participants with nonatrial fibrillation (AF) prostate cancer diagnoses who underwent radical prostatectomy between 1997 and 2011. CHADS2 scores and CCIS were used to stratify the 5‐year ischemic stroke risk. All participants were followed from the date of enrollment until ischemic stroke, death, or the end of the 5‐year follow‐up period. The 5‐year risk of ischemic stroke in the present study was 1.7%. Ischemic stroke has a better correlation with CHADS2  than CCIS . Our results show that patients with prostate cancer who underwent radical prostatectomy show significantly higher risk of ischemic stroke in high CHADS2 score patients, and the CHADS2 score could be applied for ischemic stroke prediction. Cardiovascular risks evaluation and management are suggested for prostate cancer patients with higher CHADS2 score.Patients with prostate cancer have an increased risk of stroke, but their absolute rate of stroke depends on age and comorbid conditions. The Charlson Comorbidity Index Score ( Prostate cancer is a common noncutaneous malignancy in men. Radical prostatectomy reduces cancer‐specific mortality among men with localized prostate cancer; however, important questions regarding long‐term benefit remain An association between cancer and thrombosis has been reported The CHADS2 score, which closely correlate major adverse cardiovascular events, is a popular clinical parameter used to assess the risk of ischemic stroke in patients with atrial fibrillation (AF) Prior studies have shown Charlson Comorbidity Index Score (CCIS) to be useful in predicting mortality for radical prostatectomy patients We hypothesize that ischemic stroke can be detected by using CHADS2 scores or CCIS in prostate cancer patients who have undergone radical prostatectomy, and test the hypothesis based on prostate cancer patients in Taiwan's National Health Insurance Research Database (NHIRD).The Institutional Review Board of Taichung Tzu Chi General Hospital in Taiwan approved the study protocol (REC103‐43). Because the identification numbers and personal information of the individuals in this study were not included in the secondary files, the review board waived the need for written consent.This study used NHIRD data from 1997 to 2011. Taiwan implemented a National Health Insurance (NHI) program in 1995 to provide comprehensive health care coverage. Enrollment in this government‐run, universal, single‐payer insurance system is mandatory and up to 99% of Taiwan's 23 million residents receive medical care through the NHI program. In addition, >97% of the hospitals and clinics in Taiwan are contracted to provide health care services To select potential patients in this cohort, cases of prostate cancer with radical prostatectomy were identified. AF patients were excluded. All patients with newly diagnosed prostate cancer with radical prostatectomy and follow‐up between January 1, 1997 and December 31, 2011 were included. Prostate cancer was defined according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) code 185 and radical prostatectomy was defined according to NHI Surgical Orders codes 79403B and 79410B. We used the date of radical prostatectomy as the patient's index date. We identified comorbilities and characters of patients from inpatient or outpatient files, at least in three or more consistent diagnoses in outpatient care or once in inpatient care, between the 6 months interval before index date. The scores of patient's comorbilities, such as CHADS2 scores and CCIS, were calculated by these comorbilities. In keeping with previous studies, we did not score prostate cancer as malignancy.The main outcome of the study was the occurrence of ischemic stroke , which was determined by linking records with inpatient care data of main diagnosis in the NHIRD. If a patient had multiple hospitalizations for ischemic stroke during the study period after index day, only the first hospitalization was studied. This method was used to identify people with ischemic stroke in other studies CHADS2 scores were calculated for each patient by assigning one point each for the presence of chronic heart failure, hypertension, age 75 and above, and diabetes. Two points were assigned for history of stroke or transient ischemic attack (TIA) The Charlson Comorbidity Index Score (CCIS) is a widely accepted measure for risk adjustment in administrative claims datasets The CHA2DS2‐VASc scores were calculated for each patient by assigning one point each for the presence of vascular disease , age 65–74 years, and sex category  and by assigning two points each for congestive heart failure, hypertension, age >75 years, diabetes mellitus, and history of stroke/transient ischemic attack SPSS version 15 software  was used for all data analyses. Pearson's chi‐square test was used to assess categorical variables. Continuous variables were analyzed using one‐way analysis of variance (ANOVA).The receiver operating characteristics curve was used to assess the prediction accuracy for ischemic stroke using CHADS2 scores and CCIS; plots of observed and predicted ischemic stroke were then presented. The cumulative risk of ischemic stroke was estimated using Kaplan–Meier survival curves.P < 0.05.A Cox proportional hazards regression model adjusted for patient characteristics  was used to analyze subsequent incidence of ischemic stroke during the follow‐up period. Hazard ratios (HRs) along with 95% confidence intervals (CIs) were calculated using a significance level of 0.05. Statistical significance was set at a two‐sided The NHIRD records 5414 patients diagnosed with prostate cancer undergoing radical prostatectomy from 1997 to 2011 within the NHIRD. Table P < 0.001).We further divided the prostate cancer patients into three groups based on the CHADS2 scores and CCIS  has been widely used in epidemiological studies of cardiovascular disease One population‐base study indicated that, after the introduction of prostate‐specific antigen testing in 1996, men up to age 80 with normal metabolic levels had a 13% risk of prostate cancer, a 2% risk of prostate cancer death, and a 30% risk of death from other causes, whereas men with metabolic aberrations had corresponding risks of 11, 2, and 44% Many studies have indicated that CCIS is an accurate, flexible, and easily accessible tool for predicting the mortality for patients treated with radical prostatectomy It would be very important to look to see if the CHADS2 VASc score does better or worse than the CHADS2 score since the CHADS2 VASc score is what is being used clinically now to a much greater extent than the CHADS2 score Although the findings from this study may potentially affect many patients diagnosed with prostate cancer, several caveats need to be considered. First, the diagnosis of the co‐morbid conditions is completely dependent on ICD‐9‐CM Codes. However, the NHI Bureau randomly reviews patient charts and conducts patient interviews to verify diagnosis accuracy. Hospitals with outlier charges or practices may incur audits, with subsequent heavy penalties for malpractice or discrepancies. Second, the severity of prostate cancer cannot be precisely extracted from ICD‐9‐CM codes, preventing further subgroup analysis. Third, the database does not contain information on daily activity, dietary habits, and body mass index, which may also be risk factors for ischemic stroke. Further studies linking administrative data and primary hospitalization information such as severity of ischemic stroke and detailed risk factors are warranted. On the whole, given the magnitude and statistical significance of the observed effects in this study, these limitations are unlikely to compromise the results.In conclusion, the CHADS2 score could serve as an accurate and easily accessible tool to predict the incidence of ischemic stroke in prostate cancer patients treated with radical prostatectomy. Cardiovascular risk evaluation and management should be applied more aggressively for such patients with a CHADS2 score ≥ 2.The authors have declared that no competing interests exist.Figure S1. Receiver operating characteristics curve for CHADS2, CHA2DS2, and CCIS score in prediction of stroke in prostate cancer with radical prostatectomy patients.Click here for additional data file.Table S2. Baseline characteristics of the patients with radical prostatectomy prostate cancer and without prostate cancer from 1997 to 2011 in Taiwan.Click here for additional data file.


Mycobacterium tuberculosis. Essential for viability and virulence, enzymes involved in the biosynthesis of mycolic acids represent novel targets for drug development. This is particularly relevant to the impact on global health given the rise of multidrug resistant and extensively drug resistant strains of M. tuberculosis. In this review, we discuss recent advances in our understanding of how mycolic acid are synthesised, especially the potential role of specialised fatty acid synthase complexes. Also, we examine the role of a recently reported mycolic acid transporter MmpL3 with reference to several reports of the targeting of this transporter by diverse compounds with anti‐M. tuberculosis activity. Additionally, we consider recent findings that place mycolic acid biosynthesis in the context of the cell biology of the bacterium, viz its localisation and co‐ordination with the bacterial cytoskeleton, and its role beyond maintaining cell envelope integrity.Mycolic acids are unique long chain fatty acids found in the lipid‐rich cell walls of mycobacteria including the tubercle bacillus  Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB), contains unique fatty acids termed mycolic acids  of the ketoacyl synthase FabH , and more worryingly the relatively recent reports of ‘untreatable’ TB  with either Rv0635 (HadA) or Rv0637 (HadC). Both HadA and HadC also contain a hotdog fold and functional heterodimers thus have a ‘double hotdog’ fold. These asymmetric double hotdog folds are thought to play a dual role, with one (HadB) required for catalysis, and the other (from HadA or HadC) functioning as a stabiliser of long chain acyl groups that are typical of mycolic acids. This model has been lent further credence following the recently solved 3D structure of the HadA–HadB complex  including M. smegmatis (MSMEG_6754). The role of this non‐essential dehydratase, which is a ‘fused’ HadA–HadB‐like peptide, remains unclear but it was able to compensate for the loss of hadB in M. smegmatis. The authors were able to generate a mutant of hadB in M. smegmatis only when a chromosomally integrated, and constitutively expressed second copy of MSMEG_6754 was present in the strain, indicating a functional redundancy with hadB. Interestingly, the recombinant strain did display an accumulation of shorter mycolic acid precursors, in addition to mycolic acid subspecies usually found in this fast growing species. However, the inability of this second dehydratase to rescue viability during attempts to generate a hadB knockout suggested a low level of functional protein encoded by MSMEG_6754 was present in wild‐type M. smegmatis cells growing in laboratory conditions, and implied a regulatory mechanism controlling its expression. Indeed, MSMEG_6754 was essential for survival in amoebae suggesting a specialised environmental role for this dehydratase in NTMs  for the merochain producing mycobacterial FAS‐II had been identified by the late 1990s, until late the dehydratase catalysing conversion of β‐hydroxyacyl‐ACP to enoyl‐ACP remained unidentified. The two operons harbouring the genes encoding the other three core FAS‐II components  do not include genes with homology to FabZ or FabA, the classic dehydratase or dehydratase–isomerase enzymes found in FAS‐II complexes from other bacteria , which are part of the resistance‐nodulation‐division (RND) family of efflux pumps  , indicating a role for MmpL11 in transport processes related to mycolic acid containing lipids  plays an important role in co‐ordinating polar growth; the protein localises to the tip of growing poles of mycobacterial cells and interacts with enzymes involved in cell wall biosynthesis  complex, Pks13 and FadD32, all enzymes required for the final stages of production of a nascent mycolic acid. The positions of the ACC components coincided with Wag31. However, Pks13 and FadD32 were found in a ‘subpolar’ location, suggesting that the Wag31‐associated complex and the terminal mycolate biosynthesis enzymes occupied exclusive regions of the polar tip, with new mycolic acids being deposited at a subpolar location in a growing tip.Mycobacteria, unlike other rod shaped bacteria such as , et al.  used GFP  in the mmpL3 gene. Shortly thereafter, there were two reports of drugs targeting M. tuberculosis, the pyrrole derivative BM212 and the adamantly urea compound AU1235  for function. Li et al.  result from mutations in mmpL3 as an early response to counteract toxic effects of these compounds  targeting compounds needs to be revisited. The mycolic acid biosynthesis complex represents a unique target, one that is currently rendered unexploited in MDR‐TB and XDR‐TB strains (due to resistance to isoniazid). Further work on current known inhibitors of the enzymes mentioned earlier in this section will pave the way towards new inhibitors of mycolic acid biosynthesis.Unique components of bacteria that are also essential represent ideal drug targets as they allow development of drugs that specifically inhibit the aetiological agent of disease, while leaving the normal flora intact. In this regard, , et al.  showed ti et al.  have shoin vitro studies to detect interactions between FAS‐II components and other enzymes involved in the biosynthesis process. With the advance of microscopy techniques that enable visualisation of complexes in single bacterial cells, it is likely that such complexes may one day be demonstrated in vivo. Such studies could also indicate whether biosynthesis occurs at localised complexes that also integrate transport. A better understanding is also required of the co‐ordination of this process with polar growth, and with peptidoglycan and arabinogalactan deposition in the cell wall during cell division. These studies could potentially also open alternative avenues for targeting mycolic acid assembly via the design of complex‐disrupting inhibitors. Furthermore, the regulation of mycolic acid biosynthesis has only just begun to be unravelled. Regulation of activities of FAS‐II enzymes following post translation modification by Ser/Thr protein kinase‐mediated phosphorylation has been studied extensively in vitro (Veyron‐Churlet et al., et al., et al., et al., et al., in vivo (Vilcheze et al., in vitro (Salzman et al., in vivo, resulting in changes to ratios of mycolic acid subclasses. In summary, this waxy ‘brick’ in the mycobacterial cell wall continues to be a focus of research on the tubercle bacillus, as we attempt to develop new and better therapies for this ancient disease.A majority of the enzymatic components required for mycolic acid biosynthesis have now been identified; however, there are still some stages in mycolate metabolism that remain poorly understood. These include the process of introducing double bonds in merochains, and the enzymes involved in post‐Pks13 processing of mycolic acids that ultimately leads to the formation of TMM. However, in parallel, our understanding of mycolic acid biosynthesis and transport in context of a higher order of structures has been initiated by


We examined survival in screened‐detected and non‐screen‐detected women diagnosed in the West Midlands (UK) and New South Wales  in order to evaluate whether international differences in survival are related to early diagnosis, or to other factors relating to the healthcare women receive. Data for women aged 50 − 65 years who had been eligible for screening from 50 years were examined. Data for 5,628 women in West Midlands and 6,396 women in New South Wales were linked to screening service records (mean age at diagnosis 53.7 years). We estimated net survival and modelled the excess hazard ratio of breast cancer death by screening status. Survival was lower for women in the West Midlands than in New South Wales . The difference was greater between the two populations of non‐screen‐detected women (4.9%) compared to between screen‐detected women, (1.8% after adjustment for lead‐time and over‐diagnosis). The adjusted excess hazard ratio of breast cancer death for West Midlands compared with New South Wales was greater in the non‐screen‐detected group  but not significantly different to that for women whose cancer had been screen‐detected . In this study more than one in three breast cancer deaths in the West Midlands would have been avoided if survival had been the same as in New South Wales. The possibility that women in the UK receive poorer treatment is an important potential explanation which should be examined with care. What's new?Breast cancer patients in Australia are known to have higher survival than those in the UK. In this study, the authors found that these international differences in survival persist in both screen‐detected and non‐screen‐detected groups, even after adjustment for both lead‐time bias and over‐diagnosis. These results suggest that it is essential that the mechanisms underlying these differences be understood, including potential variations in effective treatment between the two regions. We have previously shown a difference of 6% in 5‐year breast cancer survival between Australia and England for women in the target age group for screening and diagnosed during the period 1996 − 1999.The trials that led to the implementation of mammographic screening worldwide were evaluated by examining the reduction in breast cancer mortality amongst the populations of women screened.Other studies have examined the impact of mammographic screening upon individual patient survival. Analyses of survival include examinations of interval cancersExamination of survival by screening status enables us to establish, at the population level, the survival benefit afforded to women whose cancers were screen‐detected compared to women whose cancers were detected symptomatically. The disadvantage of this approach is that it is susceptible to lead‐time bias and to over‐diagnosis. Lead‐time is the additional observation time credited to women who are screen‐detected by virtue of the fact that they are asymptomatic. Breast tumours considered to be “over‐diagnosed” are those detected by screening mammography but which would not have been diagnosed during the patient's lifetime in the absence of screening.In this article, we examine net survival for breast cancer in screen‐detected and non‐screen‐detected women diagnosed in the West Midlands (UK) and New South Wales , applying a correction for lead‐time bias and over‐diagnosis. We use the results to discuss the extent to which the international differences in breast cancer survival between the UK and Australia may be explained by tumour or patient factors or to other factors relating to the healthcare women receive.The cohort of interest consisted of women who were invited to attend for screening mammography in a fully‐functioning, mature screening programme during a defined calendar period. Women diagnosed with a primary invasive breast cancer at ages 50–65 years during the period 1 January 1997 to 31 December 2006 and aged 51 years or younger on 1 January 1997 were considered eligible , the month and year of their diagnosis and death (if dead), the sub‐site, grade, histology and behaviour of the tumour, and all information pertaining to the extent of disease at diagnosis (stage). Staging information for cases in the West Midlands was recoded according to the rules used by the New South Wales Central Cancer Registry: localised (confined to the organ of origin), regional , distant (distant metastasis) and unknown stage.The cancer registry data were linked to the population‐based mammographic screening service records in each locality to establish each woman's screening status at diagnosis . We defined four categories for the screening status at diagnosis: (1) women whose cancer was detected at a routine screen, (2) women who presented with cancer following a negative screen but before being invited to their next routine screen , (3) women who presented with cancer after at least one negative screen but who had not attended their most recent appointment (lapsed attenders), and (4) women who presented with cancer who had never attended screening. We also compared women in the screen‐detected group (Category 1) to all those with non‐screen‐detected cancer . This broadly corresponded to comparing those with asymptomatic disease identified via routine screening to women presenting with symptomatic disease.Net survival is defined as the survival from the disease of interest. It is derived by adjusting the overall survival in the patient group for their expected survival in the absence of the disease. We estimated net survival using the non‐parametric Pohar‐Perme estimator,We estimated expected survival from region‐specific life tables provided by the Office for National Statistics for England and Wales and the Australian Bureau of Statisticset al.1E(s), 2E(s) … 10E(s), by assuming that survival times were exponentially distributed with a mean of E(s). Values of E(s) were subtracted from observed survival time in order to obtain corrected survival time , for the screen‐detected group, as proposed by Duffy 1E(s), 2E(s) … 10E(s) exceeded the woman's actual observed survival time, either because the predicted date of diagnosis was after 31st December 2008, or before her death. .We used the corrected survival times to estimate non‐parametric net survival for each of these ten separate data sets for the screen‐detected group. We used the rules established by Rubin1extent, 2extent … 10)extent were obtained by randomly and independently selecting values of extent of disease from the donor group.Data on extent of disease were missing for 8.9% of women diagnosed in West Midlands and 5.3% of those diagnosed in New South Wales. We used a 10‐fold hot‐deck approach to take account of these missing values for extent of disease. The hot‐deck approach involves identifying ‘donor groups’ for each woman with missing information on extent of disease. The donor group for each woman comprised women diagnosed in the same period  and region , at a similar age  who had been followed for a similar amount of time , and with the same vital status at the end of follow‐up , and screening status . For each woman with missing data, ten separate values of extent of disease  for the screen‐detected only (where i = 1–10).Combining these two procedures resulted in data sets with a set of 10 imputed values for the variable 1 to E(s)10E(s) combined with 1extent and one model for the non‐screen‐detected cancer using observed survival times and values of 1extent. A priori, we included age at diagnosis, region and extent of disease in the models. We used a reduction of 3 or more in the AIC (Akaike Information Criterion) to indicate a better fit. We examined non‐linearity of age by the inclusion of restricted cubic splines and tested for time‐varying effects for region, age at diagnosis, and extent of disease. We examined interactions between region and age, and between region and extent of disease.We fitted flexible non‐parametric regression models for net survivaliE(s) and jextent (100 separate combinations of results). For non‐screen‐detected women we refitted the model found to fit best to using values of 1extent to the data for 2, extent3 … extent10extent (10 sets of results).For the screen‐detected group, we applied the model with the smallest number of parameters to each unique combination of We predicted from the final models estimates of crude mortalityb).We used Rubin's rulesThe estimates of crude mortality were used to establish the number of cancer deaths that could have been avoided in the hypothetical situation in which survival was equalised between the two regions. This provides an estimate of the public health impact of survival differentialsWe analysed data for 5,628 women in West Midlands  and 6,396 women in New South Wales . Those excluded were the very small number of women who were known to the registry only because breast cancer had been mentioned on their death certificate (DCOs) or because the sequence of dates provided was illogical. The proportion of tumours that were screen‐detected was greater in West Midlands . In West Midlands, however, the survival of women diagnosed with interval cancers was not dissimilar to that of lapsed attenders, whilst those who had never attended had the worst survival (Table b). The difference in net survival between West Midlands and New South Wales was greater among non‐screen‐detected women (4.9% five years after diagnosis) than among screen‐detected women in the two regions  were constant over follow‐up time and followed a log‐linear form. The effect of age upon survival amongst non‐screen‐detected women was non‐linear. The effect of both age and extent of disease were found to change over follow‐up time amongst non‐screen‐detected women. The excess hazard of death from breast cancer within five years of diagnosis in the baseline model was 57% higher among women diagnosed in the West Midlands than women in New South Wales  amongst non‐screen‐detected women and 36 (29.5%) amongst those whose cancer was screen‐detected  than New South Wales , which is fully consistent with our previous findings.In the West Midlands, 5‐year survival amongst women who had never attended for screening was 4.9% lower (absolute difference) than amongst the never‐attenders in New South Wales. For women whose cancer was screen‐detected, this difference was 1.7% after adjustment for lead‐time bias.The 5‐year adjusted excess hazard ratio of breast cancer death for the non‐screened group indicates a substantial and significant survival disadvantage for West Midlands. This is striking because these estimates are adjusted for differences in age and extent of disease at diagnosis, and so one might expect survival to be much more similar. Even among screen‐detected women the survival disadvantage is distinct which is particularly striking because these are women diagnosed with asymptomatic cancers. Their tumours are predominantly localised, and as such they would almost all be treated surgically and with curative intent and have a high chance of long‐term survival.Although the overall number of deaths is relatively modest in this cohort of cancer patients, with only 9.1% of all women dying during follow‐up, the impact of these differences is important. The increased excess hazard of breast cancer death 5 years after diagnosis in the West Midlands is double that of New South Wales amongst non‐screen‐detected women and 72% greater amongst those with a screen‐detected cancer. Overall we estimated that more than a third of the deaths attributable to breast cancer observed for women in West Midlands would have been avoided had their survival been the same as the women in New South Wales.Taken together, our results suggest that differences in screening practice and extent of disease at diagnosis do not explain the overall difference in survival between West Midlands and New South Wales for this age group, and that women with breast cancer in West Midlands have a higher risk of excess death from their cancer than women in New South Wales, whether they are screened or not.de facto screening, , but it is unknown to what degree this occurs.These differences in survival are likely to be in part due to the differences in the way screening is delivered in the West Midlands and in New South Wales. In the UK, the National Health Service is free at the point of delivery for the whole population and private mammography is rare. In contrast, in Australia, mammography is obtained through BreastScreen Australia but also through private radiology clinics. Mammograms conducted privately for diagnostic purposes, rather than in asymptomatic women, may be refunded via the Medicare Benefits Scheme (MBS). A substantial proportion of those conducted in private clinics is likely to constitute de facto screening to explain the difference in survival for the non‐screen‐detected group. We randomly reallocated women in New South Wales with localised disease from the non‐screen‐detected to the screen‐detected group, for selected proportions ranging from 1% to 95%, and then re‐estimated the net survival function. Over 100 iterations the five‐year net survival estimates for the non‐screen‐detected group in New South Wales became similar to those for West Midlands only when an implausible 90% of the localised cancers  were reallocated (data not shown). This level of reallocation would require that the true proportion of cancers screen‐detected in New South Wales in the cohort was in excess of 64%, in comparison to the 36.5% actually observed . For smaller, but substantial proportions of reallocation the reduction in the survival difference was relatively small. We thus consider it very unlikely that de facto screening can fully explain the difference in survival between non‐screen‐detected women. This analysis also served to illustrate the robust nature of the difference for the screen‐detected group: there remained a survival advantage for New South Wales, albeit very small, even when 95% of localised (apparently symptomatic) tumours were reallocated to the screen‐detected group.Although it is probable that we incorrectly allocated some women to the never‐attender group who were actually screen‐detected, especially in New South Wales, information on their personal characteristics and the features of their cancer would not have been compromised since these data items were collected from the Cancer Registry, rather than via the screening service. It is possible, however, that this may have biased our estimates of net survival. We therefore performed a sensitivity analysis to examine the potential for p value 0.07) This supports the interpretation that the breast cancer survival differences between New South Wales and West Midlands cannot be fully explained by the shorter screening interval in New South Wales.We may consider whether the longer screening interval in the West Midlands compared with New South Wales (3 years versus 2 years) might contribute to these differences. Screen‐detected cancers in New South Wales could perhaps be diagnosed at an earlier stage, with better prognosis. However, the distribution by extent of disease was similar in both regions; the proportion of localised disease was in fact slightly higher in the West Midlands than in New South Wales  greater delays in diagnosis in West Midlands, (b) longer waiting times for hospital consultation for non‐screen‐detected cancers or (c) less effective screening in West Midlands than in New South Wales. The fact that differences persist after adjustment for extent of disease at diagnosis does not support any of these explanations, however. It is theoretically possible that residual confounding may partially account for this lack of explanatory power. Residual confounding may have arisen due to the fact that the screening interval for women in West Midlands is longer than in New South Wales, combined with a tendency for the accuracy of the ‘extent of disease’ variable for non‐screen‐detected cancers to be lower in West Midlands. Together, this would imply that within each stage grouping, the true (unknown) stage of disease is more advanced in West Midlands than in New South Wales (stage migration).These findings thus tend to refute the idea that breast cancers in West Midlands and New South Wales are very different at the point of diagnosis, but are subsequently treated with similar effectiveness. We consider a much more likely explanation for our findings is that the tumours themselves are not substantially different, but that something different happens once the woman is diagnosed. This is of greater concern: we have examined a group of young women with predominantly localised disease. Almost all of them would have had treatment with curative intent.Treatment may vary with comorbidity, and is subject to patient compliance, but there is no particular reason to assume that these would persistently differ between New South Wales and West Midlands, particularly in this age group. The alternative explanation is that the treatment in West Midlands is not as effective as those in New South Wales leading to poorer stage‐specific survival.Although overall survival was high for this cohort of women, our data suggest that more than one in three breast cancer deaths within five years of diagnosis in the West Midlands would be avoidable if five‐year survival were the same as in New South Wales. The women we analysed here are relatively young. They are therefore less likely to be suffering from other serious illnesses and more likely to be economically and socially active. In order to improve the prognosis for women diagnosed with breast cancer in the UK during their early 50s it is essential that we understand better the mechanisms that underlie these international differences in stage‐adjusted survival. Differences in the effectiveness of treatment are an important possibility and they now deserve to be examined with great care. It is not possible to dismiss differences in breast cancer survival between the UK and other countries such as Australia as artefactual.


England between 2002 and 2010 reduced adult smoking prevalence and cigarette consumption.To examine whether government‐funded tobacco control television advertising shown in Analysis of monthly cross‐sectional surveys using generalised additive models.England.England and interviewed in the Opinions and Lifestyle Survey.More than 80 000 adults aged 18 years or over living in GRPs—a measure of per capita advertising exposure combining reach and frequency), cigarette costliness, tobacco control activity, socio‐demographic variables.Current smoking status, smokers' daily cigarette consumption, tobacco control gross rating points , was associated with 3% lower odds of smoking 2 months later  and accounted for 13.5% of the decline in smoking prevalence seen over this period. In smokers, a 400‐point increase in GRPs was associated with a 1.80%  reduction in average cigarette consumption in the following month and accounted for 11.2% of the total decline in consumption over the period 2002–09.After adjusting for other tobacco control policies, cigarette costliness and individual characteristics, we found that a 400‐point increase in tobacco control England between 2002 and 2010 was associated with reductions in smoking prevalence and smokers' cigarette consumption.Government‐funded tobacco control television advertising shown in  Most of the evidence in favour of the effectiveness of mass media campaigns (MMCs) in reducing adult smoking prevalence and cigarette consumption comes from studies evaluating campaigns run for short time‐periods in countries with little other tobacco control (TC) activity. These studies, conducted in the mid‐1970s to mid‐1990s Fig. S1 in Supporting information), including large‐scale TC MMCs, and since 2007 the United Kingdom has been identified as having the most comprehensive TC policies in Europe In the United Kingdom, the effectiveness of TC MMCs on smoking prevalence and consumption has been evaluated only for short‐term campaigns targeting either a specific population subgroup In this paper we evaluate whether government‐funded TC television advertising shown in England between 2002 and 2010 was associated with changes in smoking prevalence and cigarette consumption. Our focus is on television advertising, as this media channel accounts for both the major expenditure and exposure by far. This study will provide essential evidence for determining whether the cutting of government spending on MMCs is justifiable.The Opinions and Lifestyle Survey (OS), a monthly cross‐sectional survey run by the Office for National Statistics Respondents were asked about their tobacco use. Smokers were defined as those answering ‘yes’ to the question: ‘Do you smoke cigarettes nowadays?’. Measures of cigarette consumption were based on responses to two questions: ‘How many cigarettes a day do you usually smoke at weekends?’ and ‘How many cigarettes a day do you usually smoke on weekdays?’. We derived an average number of cigarettes smoked per day by taking a weighted average of weekend and weekday consumption using weights of two‐sevenths and five‐sevenths, respectively (hereafter defined as average consumption). Respondents are also asked questions about their age, gender, government office region of residence (GOR), employment, education and gross income.A coding scheme was used to quantify TC activity in England each month. The scheme was based on four policies: (i) smoke‐free work and public places, (ii) bans on advertising and promotion, (iii) health warning labels on cigarette packets and (iv) treatment to help smokers stop. Scoring for each policy was identical to that assigned by the Tobacco Control Scale Box S1 in Supporting information) and income using monthly income reported by the OS respondent. Monthly income reported by respondents in the OS surveys represents their total gross income from all sources  before deductions for income tax, National Insurance, etc.Cigarette costliness is defined as the ratio of the cigarette price to income in the same month. We measured price using the weighted average retail selling price of cigarettes (Gross rating points (GRPs) are a standard measure of average per‐capita advertising exposure and are commonly used in evaluations of televised MMCs Information on month and year of interview of the OS respondents aged 18 and over, living in England and interviewed in the OS between February 2002 and April 2010 inclusive (no OS survey conducted in January 2002) was used to match the survey data to information on GRPs, cigarette prices and the tobacco control score. Records with missing data on smoking status were excluded from both the smoking prevalence and cigarette consumption analyses (<0.05%). The analysis of cigarette consumption was restricted further to respondents who self‐reported as being a smoker. Both daily and non‐daily smokers were included. Records with missing consumption data were also excluded from this analysis (<0.4%).We modelled the relationship between GRPs and smoking prevalence using a binomial logistic generalized additive model. Given that we expected non‐linear relationships between some of the explanatory variables in the model and the outcome variable, this model allows a flexible specification of the dependence of the response on the covariates rather than restriction to parametric relationships.As evidence suggests that TC media campaigns have their effect on smoking behaviour while campaigns are being broadcast and for a short time afterwards Table S2 in Supporting information). The number of individuals who declined to take part or could not be contacted in the OS survey rose slightly during the study period from approximately 30% to 40%. In order to compensate for non‐response, OS introduced non‐response weights based on age, sex and GOR of residence in April 2005. Rather than use these weights, which are only available from mid‐way through our study period, our inclusion of individual‐level variables for age, gender and GOR adjusted for non‐response bias over the entire study period.To model the effect of other TC strategies, we included the tobacco control score as a categorical term and individual cigarette costliness as a cubic regression spline to allow for a non‐linear relationship. As monthly income reported by OS respondents was skewed strongly to the right, we log‐transformed cigarette costliness before analysis to make the distribution more symmetrical. All models also included the following individual‐level variables associated with smoking rates: cubic regression splines for age and income, and categorical variables for gender, GOR, education, employment status and social class . We therefore included number of adults per household as a linear term to adjust for this unequal probability of selection.Table S2 in Supporting information).We modelled the relationship between GRPs and average cigarette consumption using a Poisson generalized additive model, and included the same covariates as in the prevalence model  or evidence that real overdispersion exists . We included all major factors that determine tobacco use, considered non‐linear effects of covariates in our models and checked for outliers to minimize model misspecification, yet there was still a small amount of overdispersion detected. We therefore corrected the standard errors using a quasi‐Poisson model.All models were fitted in R using the gam function from the library mgcv We used the fitted models to investigate what impact GRPs had on cigarette consumption and smoking prevalence during the study period. With the original data, we predicted cigarette consumption for individuals in 2002 and 2009 and computed the percentage reduction in the model‐predicted yearly means . To predict the percentage reduction had there been no TC advertisements during the time‐period, we repeated this procedure but set GRPs = 0 in the original data set. Similarly, the difference in annual smoking prevalence predicted for 2002 and 2009 was compared between the original GRP data and with GRP = 0.During the period covered by the study, average daily consumption and smoking prevalence has been declining and TC efforts have increased, with both the tobacco control score and weighted average price of a packet of 20 cigarettes increasing over time Figs . GRPs, iOver 84 months between February 2002 and April 2010, inclusive, 19 488 and 81 256 adults were included in the consumption and smoking prevalence analyses, respectively. The smooth terms for GRPs at 1 month and 2 months previous were found to be linear (EDF = 1) in both the smoking prevalence and consumption models, and were replaced with linear terms.After controlling for other TC strategies and cigarette costliness, as well as individual characteristics, smoking prevalence was associated with GRPs 2 months previous Table . A 400‐pAfter controlling for other TC strategies, cigarette costliness and individual characteristics, average consumption was associated with GRPs in the previous month Table . A 400‐pP > 0.1 for all smooth terms).The sensitivity analysis using NRT GRPs did not change our results, and we found no effect of NRT GRPs on either smoking prevalence or consumption  2 months later and a 1.80% reduction in average consumption in the following month. The association between GRPs and smoking prevalence was only just statistically significant at the 5% level, but is supported by research in Australia Although we adjusted for all the known major factors that determine tobacco use , we cannot completely rule out unmeasured confounding which could have occurred via unmeasured, unknown variables that confound the relationship between GRPs and smoking outcomes or via imperfect measurement of the major factors included in our analysis.We used population‐level data for GRPs and weighted average price (WAP), and our model assumes that there is no individual‐level variability. In reality, GRPs measure average potential exposure; individual‐level exposure will vary depending on frequency of actual television viewing and attention to advertisements. Similarly, WAP does not reflect the brand smoked by an individual and we know that price trends have varied markedly by brand This is the first study in Europe to look at the effect of TC advertisements on smoking prevalence and consumption and the first international study to examine impacts on consumption in an environment of intense TC activity. In contrast to the aforementioned study in Australia The population impact of a TC intervention is measured in terms of effectiveness × reach. While the effect of a mass media intervention per smoker may be small, it has the potential to reach a large proportion of smokers and thus to have a significant population impact. Therefore, although the effect sizes estimated here are small, they translate to an appreciable impact at the population level. We estimate that, over the period 2002–09, 11.2% of the decline in cigarette consumption and 13.5% of the decline in prevalence was attributable to the impact of MMCs. The decline in smoking prevalence observed via our model equates to approximately 27 500 fewer smokers per year, and thus to appreciable health gain. Government expenditure on television advertising campaigns from January 2002 to December 2009 was £78 million, equating to £406 per additional non‐smoker based on our model predictions. Importantly, this figure considers only the direct impact of MMCs on smoking prevalence. Evidence suggests that MMCs might have indirect effects Although the majority of campaign exposure for the population is still on television, the media environment changed during the study period  and will continue to evolve. As such, the reach of these campaigns may have reduced over time. Research on the most efficient use of various media types in this changing environment, refining media message and intensity and the cost‐effectiveness of campaigns are research goals for the future.In conclusion, TC television advertising is associated with reductions in smoking prevalence and consumption, even in a jurisdiction with comprehensive TC policies.None.Box S1 Weighted average price of cigarettes.Figure S1 Time‐line of tobacco control policies in England, 2000–10.Table S1 Coding scheme for tobacco control policies.Table S2 Predictors included in generalized additive models.Click here for additional data file.


Shorter sleep is a risk factor for weight gain in young children. Experimental studies show that sleep deprivation is associated with higher nighttime energy intake, but no studies have examined the patterning of energy intake in relation to nighttime sleep duration in young children.The objectives of the study were to test the hypothesis that shorter‐sleeping children would show higher nighttime energy intake and to examine whether the additional calories were from drinks, snacks or meals.Gemini twin cohort, using data from one child per family selected at random to avoid clustering effects. Nighttime sleep duration was measured at 16 months of age using the Brief Infant Sleep Questionnaire. Energy intake by time of day and eating episode  were derived from 3‐day diet diaries completed when children were 21 months.Participants were 1278 families from the P < 0.001), with those sleeping <10 h consuming on average 120 calories (15.2% of daily intake) more at night than those sleeping ≥13 h. The majority of nighttime intake was from milk drinks. Associations remained after adjusting for age, sex, birth weight, gestational age, maternal education, weight and daytime sleep.Consistent with our hypothesis, shorter‐sleeping children consumed more calories at night only (linear trend Shorter‐sleeping, young children consume more calories, predominantly at night, and from milk drinks. Parents should be aware that providing milk drinks at night may contribute to excess intake. This provides a clear target for intervention that may help address associations between sleep and weight observed in later childhood. Short sleep has been associated with obesity risk in children, adolescents and adults, but the association appears stronger at younger ages Both higher energy intake and lower energy expenditure are potential pathways linking shorter sleep to adiposity. However, sleep is an energy‐efficient activity and the fact that more hours of wakefulness promote weight gain particularly implicates higher energy intake as the underlying mechanism ad libitum access to food increased energy intake at night, particularly during the hours of extended wakefulness Increased time available for consumption has been proposed as one of the mechanisms that could drive higher energy intake in shorter sleepers The aim of the present study was therefore to test the hypothesis that energy intake would be specifically higher in the evening in shorter‐sleeping children by examining the temporal patterning of energy intake in relation to sleep duration using 3‐day dietary data. The secondary aim was to determine whether the additional calories were from meals, snacks or drinks.n = 1278, 53.2% baseline sample). Children with complete data had mothers who were slightly older (34.6 vs. 32.5 years), had slightly lower BMI at baseline (24.7 vs. 25.6), had slightly higher education levels and were more likely to be from a white ethnic background . Intraclass correlations between twins for the sleep and dietary data were high; therefore, to avoid clustering effects by family, one twin was randomly selected from each pair. Informed written consent was provided by all parents. Ethical approval was granted by the University College London Committee of Non‐National Health Service Human Research.Participants were 1278 families participating in the Gemini study. Gemini is a representative population‐based cohort of twins set up to examine early life influences on growth Sleep was assessed when the children were approximately 16 months old  using an adapted version of the Brief Infant Sleep Questionnaire Dietary data were collected when children were on average 21 months old . Parents were sent diet diaries with detailed instructions and asked to record everything their child ate or drank inside or outside the home for 3 d, including two weekdays and one weekend day while in their care. Where possible, parents were asked to provide household measures and packet weights to estimate food consumed. In the absence of this, parents were asked to provide portion size estimates using instructions from a photographic booklet P < 0.001).All food and drink items within a single time entry constituted an eating occasion. Each occasion was coded as a meal or snack using a food‐based classification system Nutrition data were coded by researchers at the Medical Research Council Human Nutrition Research unit. Energy intakes were estimated by matching each food reported in the diary to a food in the National Diet and Nutrition Survey food composition database then multiplying it by portion size estimate from the information provided by the parent. Some general dietary questions were included with the diaries to assist in coding missing data. Dietary variables examined were total energy intake  and energy intake from meal, snack and drink occasions . This method of dietary data collection outperforms alternative unweighed methods and has been validated against weighed dietary records in children aged 6 to 24 months Sex, birth weight, gestational age and maternal education were reported by the primary caregiver in baseline questionnaires. Maternal educational attainment was dichotomized to lower  or higher . Gemini families are asked to provide weight measures every 3 months; weight data close to sleep data were available for 84% of the sample.anova) compared energy intake between the five sleep groups for each time period. Tests of a linear association  were used to determine whether energy intake increased or decreased with sleep duration for any given time period.Dietary and sleep data were normally distributed. To examine the temporal patterning of energy intake, the 24‐h dietary day was divided into four periods designed to cover the range of conventional meal/snack intakes for young children in the UK: morning (6:00 to <10:00 h), daytime (10:00 to <15:00 h), afternoon/evening (15:00 to <19:00 h) and night (19:00 to <6:00 h). The start of the nighttime period was fixed according to the average bedtime in the sample (19:12 h) and the end of this period was slightly before the average wake time of 6:50 in the morning. Univariate analysis of variance . Daytime sleep is common in children of this age and was included as a confounder in case shorter nighttime sleepers were compensating with more sleep during the day. Data from boys and girls were combined as there were no significant interactions between energy intake, sleep and gender.Participants' characteristics by nighttime sleep duration are presented in Table lower in the shorter‐sleeping groups in a linear fashion (P = 0.023), although the absolute differences were small; children sleeping <10 h a night consumed 319 kcal, while children in the longest‐sleeping group consumed on average 360 kcal, a difference of 41 kcal. Energy intake at night (19:00 to <6:00 h) was significantly higher in the shorter sleepers in a linear fashion (P < 0.001). Children in the shortest‐sleeping group (<10 h a night) consumed on average 166 kcal at night, while those in the longest (>13 h) consumed 46 kcal, an average difference of 120 kcal.Dietary data by sleep duration are presented in Table Over 90% of calories consumed during the nighttime period, across all sleep groups, were consumed before 12 midnight .P's <0.001; Table Across all drink occasions recorded, 76.8% were milk drinks and 98.6% of all calories in drink occasions came from milk drinks. Partitioning energy intake at night into energy obtained from meals, snacks and drinks showed that shorter sleep was associated with higher intake from all occasions P's <0.00; Table 1Associations between sleep and energy intake (by time of day and eating occasion) were unchanged after adjusting for age, sex, birth weight, gestational age, maternal education, weight and daytime sleep (data not shown).This study shows that shorter sleep in young children is associated with higher energy intake between 19:00 and 6:00 h and that the majority of additional intake comes from milk drinks consumed before midnight. This finding persisted after adjustment for multiple potential confounders. Nighttime consumption of milk drinks may be an important means through which shorter sleep contributes to adiposity and provides a potential target for intervention.This is the first study to demonstrate that sleep is associated with temporal variations in energy intake in children. The findings are consistent with experimental data in adults and older children which have shown that consecutive nights of sleep restriction encourage the consumption of additional energy at night We show that shorter‐sleeping children had both a later bedtime and an earlier wake time, but only consumed more than longer sleepers in the nighttime period. It may be that having more time to eat, particularly at night, contributes to excess energy intake among shorter sleepers. This could help explain why both short sleep and later sleep timing have been associated with weight gain in prospective studies of children In this sample, the majority of nighttime calories were obtained from drinks, of which over 98% were from milk drinks. While we did not assess why parents were giving their children additional drinks at night, one possibility is that hunger levels may be endogenously higher in the evening Feeding before bed or during the night may be good practice in infants who are exclusively milk‐fed, but after weaning this should no longer be necessary This study benefited from detailed food diaries that recorded multiple days of dietary intake and included a timed entry for each eating occasion. This is the largest dietary data set of its kind for young children in the UK. The use of parent reports of sleep behaviour is a limitation, but this method is commonly used in large‐scale cohorts where objective measurements are not feasible There was a slight time lag between the sleep (16 months) and dietary assessments (21 months). However, nighttime sleep duration is relatively stable at this age A complex relationship exists between sleep, circadian rhythms and energy regulation Although participants were from a cohort of twins, we included data from one twin selected at random from each pair. There is no reason to expect that any of the observed associations between sleep and eating behaviour would differ in singletons. It is also reassuring that the average daily caloric intake in this sample is comparable with the reference values for children of this age We show that shorter‐sleeping children consume more energy than longer sleepers, that this is predominantly after 19:00 h, and the majority of the extra calories come from milk drinks. This could lead to simple guidance to parents to avoid excess energy intake in nighttime drinks for young children.No conflict of interest was declared.LM conceptualized the study, carried out the data coding and data analysis, drafted the initial manuscript and approved the final manuscript as submitted. JW conceptualized and designed the Gemini study, designed the data collection instruments, critically reviewed and revised the manuscript and approved the final manuscript as submitted. CHL supervised the data analysis, critically reviewed the manuscript and approved the final manuscript as submitted. LJ designed the dietary data collection instruments, coordinated the data collection, critically reviewed the manuscript and approved the final manuscript as submitted. CHMvJ supervised the data collection and data coding, critically reviewed the manuscript and approved the final manuscript as submitted. HS assisted with the data coding, critically reviewed the manuscript and approved the final manuscript as submitted. AF conceptualized the study, supervised the data analysis, critically reviewed and revised the manuscript and approved the final manuscript as submitted.Gemini was funded by a grant from Cancer Research UK (C1418/A7974). Coding of the diet diaries was supported by funding from DANONE Baby Nutrition (Nutricia Ltd). Laura McDonald is funded by a UK Medical Research Council PhD studentship.