Reforms giving users of public services choice of provider aim to improve quality. But such reforms will work only if quality affects choice of provider. We test this crucial prerequisite in the English health care market by examining the choice of 3.4 million individuals of family doctor. Family doctor practices provide primary care and control access to non‐emergency hospital care, the quality of their clinical care is measured and published and care is free. In this setting, clinical quality should affect choice. We find that a 1 standard deviation increase in clinical quality would increase practice size by around 17%. The relevant effect for assessing the potential incentive for practices to improve quality is the increase in the number of individuals who wish to join a practice when its quality increases. Around 25,000 adults aged 25 and over live within 2 km of the average practice, so even small changes in the probability that an individual will choose a practice can lead to a sizeable increase in the practice list. Using our most conservative estimates, an increase of 1 standard deviation (SD) in clinical quality will increase the number of individuals over the age of 25 choosing a practice by just over 900, or around 17% of the mean number of such patients in a practice.et al., et al., et al., et al., et al., et al., et al., Our results contribute to the literature on choice in health care.et al., et al., et al., There are few studies of the effects of quality on patient choice of family doctors, perhaps because of the previous lack of good data on quality. Research has focused on other attributes of care or proxies for quality. Studies have shown the importance of distance  practice, which also acts as the gatekeeper for elective (non‐emergency) hospital care. GPs are not employees of the NHS , the local NHS organisations responsible for the administration of primary care in their area). GPs work in general practices, most of which are limited liability partnerships owned by the GPs. The NHS contracts with the general practices, not with the individual GPs. English practices have on average 4.2 GPs and around 6,600 patients  contract whose terms are set by national negotiations between the NHS and the British Medical Association (the doctors’ trade union). GMS practices are paid a mixture of lump sums, capitation, quality incentive payments and items of service. Around 75% of practice revenue varies with the number of patients registered with the practice. Over 50% is from capitation payments determined by a national formula which takes account of the demographic mix of practice patients and local morbidity measures. Quality incentives from the Quality and Outcomes Framework (QOF) Roland,  generateThe remaining practices have a Primary Medical Services (PMS) contract which is negotiated between the practice and their local PCT. The practice receives a lump sum for agreeing to provide similar services to those required under the GMS contract, plus additional services for particular patient groups. The amount received is typically what the practice would have received under GMS, plus an addition to cover the cost of the extra services. PMS practices also receive QOF payments, though they are paid less than GMS practices for the same quality achievement because some of the QOF payments relate to activities which are also paid for directly under PMS contracts. As under GMS the practice has to meet its expenses from its revenue. Thus, whether the practice has a GMS or a PMS contract, its total revenue will increase with the number of patients. A rough estimate under the assumption that average revenue and cost per patient are constant is that an additional patient registered with the practice produces revenue of £135, expenses of £80 and net income of £55 per practice partner.Although practices cannot refuse to accept patients on grounds of race, gender, social class, age, religion, sexual orientation, appearance, disability or medical condition, patients face two constraints when choosing a practice. First, a practice can refuse to accept patients who live outside a catchment area agreed with its PCT. Second, practices can notify their PCT that their list is closed: if this is the case, no new patients will be accepted for a period of between 3 and 12 months. Around 2% of practices have closed lists at any one time.et al., One of the strands in policy in the English NHS in recent years has been the promotion of competition amongst hospitals and, latterly, amongst general practices , General Medical Statistics, QOF, Hospital Episode Statistics) with small area census and socio‐economic data from Neighbourhood Statistics. Sources are in Appendix Table C1.2.1The ADS contains, for each administratively defined homogenous small geographical area in England , the number of patients by age/sex band who are registered with each general practice at 1 April 2010. There are 32,482 LSOAs in England, with a minimum population of 1,000 and a mean population of 1,500.The ADS data contain age (in bands) and gender of each patient. We attribute socio‐economic characteristics to patients by their LSOA of residence. The characteristics include the proportion of the LSOA who are income deprived , the proportion of adults with no formal educational qualifications, the proportion who report themselves in fair or good, rather than poor, self‐rated health and the proportion who are of Asian ethnicity. We also categorise an LSOA as urban or rural and by the annual rate of inward migration. These patient and small area‐level variables allow us to examine whether different types of patient have different preferences over practice characteristics.2.2We use data from the GMS census (taken on 30 September 2010 and 2009) to measure the average age of GPs, the proportion of female GPs and the proportion of GPs qualified in the UK, in Europe, in Asia and elsewhere. We also have data on the type of practice contract (PMS or GMS) and whether the practice has opted out of providing out‐of‐hours care for its patients.2.3et al., Our measure of practice clinical quality is from the QOF. The QOF is a national pay‐for‐performance scheme introduced in April 2004 and whose broad structure has been maintained subsequently. The quality indicators in the QOF were chosen on the basis of evidence about the effects of the activities they measure on patient health. Higher achievement of the quality indicators has been shown to be associated with fewer emergency hospitalisations for conditions which should be managed in primary care  is a vector of K observed variables and εiaj is random error term reflecting practice characteristics observed by the individual i but not the econometrician. Each resident i in LSOA a chooses the practice in their choice set Ca which yields the highest realised value of uiaj.Suppose that utility for individual εiaj errors are independently and identically distributed according to the type 1 extreme value distribution, then  will affect choice probabilities and thus the probability of choice of practice j by an individual in LSOA a is the same for all individuals in LSOA a. Hence, j in choice set Ca is weighted by the number of individuals in LSOA a who choose practice j.If we assume, as in most of our models, that individuals’ preferences over practice characteristics do not vary across different types of individual, only variables which vary by LSOA and practice ]. They estimate the marginal utility from the practice characteristics only up to a positive scalar since uiaj∗=λuiaj=λx′iajβ+λεiaj yields the same choices as uiaj for all λ > 0. Since the scale is determined by the variance of the sample on which the model is estimated, we also report the marginal rate of substitution between practice quality qj and the distance daj in kilometres between the LSOA centroid and the practice. MRSqd is the additional distance in kilometres that a patient in LSOA a would be willing to travel to practice j if its quality increased by 1 point. Since it is the ratio of marginal utilities it is invariant with respect to the scale of utility. Thus comparison of the MRSqd for different samples of patients conveys information about differences in preferences.The reported coefficients have the same sign as the effect of an attribute on the probability of choice Table In Appendix A, we examine the robustness of these results to a large set of alternative measures of quality. These include total QOF points for other years, sub‐domains of the QOF, the practice rate of hospital admissions for conditions which ought to be manageable in general practice and patient satisfaction measures based on practice surveys. The results confirm that patients are more likely to choose practices with higher quality. Total QOF points for 2006/7 perform a little better than total points for 2010/11 or the 2006/7–2010/11 average of total points. Many of the other measures of quality are insignificant conditional on inclusion of total 2006/7 QOF points. We therefore retain our baseline single‐quality measure  because it is correlated with other measures of quality (see Appendix Table C4), is simpler than models with multiple quality measures and has a better fit than other models with a single quality measure. Total QOF points are also plausible as a measure which affects patient choices since these were publicly reported on the NHS Choices website aimed at informing patient choice.Because of the importance of distance in determining practice choice we investigated the robustness of the baseline model to alternative polynomial functions (linear to quintic) of distance. Figure 4.2et al., We begin by examining observed heterogeneity by age and gender. Previous literature has suggested that preferences for medical practitioners differ across men and women and individuals of different ages. We estimate separate models for 12 age and gender groups, using the same specification as the baseline model. We report the quality and distance coefficients in Table Table et al., These differences in the weight that individuals in different types of LSOA place on quality when choosing a practice seem plausible. Individuals in rural areas expect to travel further for all types of services, so they are more willing to travel for a practice with higher quality than patients in urban areas. Residents in LSOAs with fewer income‐deprived individuals may be more able to take time off work without incurring a financial penalty and so be less concerned about the travel time required to use their GP than residents in LSOAs with higher income deprivation. Residents of LSOAs with better educated populations may be better able to find, and interpret, measures of practice quality. Our finding that patients in LSOAs with healthier populations place a higher value on quality relative to distance than those in LSOAs with less healthy populations again supports a travel cost story: less healthy patients want to be closer to their practice because they expect to visit it more frequently. The fact that Asian or British Asian residents on average have more morbidity for many conditions treatable in primary care such as diabetes and heart disease (Scarborough versus 3.0 metres). We, therefore, feel that the estimates from the simpler conditional logit baseline model are a reasonable representation of average preferences over practice characteristics.The models in Tables 4.3In interpreting the results, we assume that practice lists reflect patient preferences rather than practice decisions. But, as noted in Section et al., The second constraint on patient choice of practice is that practices can, with the agreement of their PCT, formally close their lists to new patients for between 3 and 12 months at a time. Some practices also declare informally that their lists are ‘open but full’ and they are not accepting new patients. However, for practices intending to stay in business, list closures must be temporary since each year around 8% of patients will leave a practice list   down to 8 km, 6 km, 4 km and 2 km, which allows us to compare estimates across radii.A finding of little difference in estimates across models suggests that our results reflect patient choices and preferences rather than GP rationing of demand via catchment areas and list closure. Table In sum, the similarity of the estimates across the models in Table 4.4It is possible that practice quality is determined in part by the demographic, socio‐economic and health characteristics of the patients on its list. For example, some patient population may make it easier for practices to achieve their QOF targets. Further, if different patient types have different preferences over practice characteristics, the quality measure may be correlated with unobserved demand factors. Endogeneity bias could go either way: practices which are better could attract more complex patients with whom it is more difficult to achieve QOF points, or better educated individuals who may be easier to treat may be more likely to choose better practices. Our use of a lagged measure of practice quality should reduce this source of endogeneity bias. However, measurement error and unobserved practice characteristics affecting demand and correlated with quality may also contribute to endogeneity.To allow for possible endogeneity from all sources, we estimate a model in which we instrument practice quality. To motivate our choice of instrument, we use the fact that PCTs had clinical governance responsibility for their practices. They monitored the prescribing of their practices, inspected their premises, audited their QOF returns, provided financial assistance for practice computing and financed community nurses to complement GP services. They also provided information to practices comparing their performance with other practices in the PCT. All these activities were intended to increase quality of care in their area and so are likely to affect the QOF performance of all their practices. This suggests that the average quality of all other practices in the PCT is likely to be a good predictor of a practice's quality. We therefore use this instrument to examine the possible bias in our estimates due to endogeneity.et al., et al., We implement our IV estimates using two‐stage residual inclusion  in quality. The absolute values of the effects of a 1 SD increase are roughly of the same order of magnitude for quality, the average age of GPs and the percentage of non‐European qualified GPs. By contrast, increasing the percentage of female GPs by 1 SD  has a much smaller effect on individuals’ willingness to travel further to a practice.Columns (4)–(6) examine the estimated effect on practices. Column (4) reports the average marginal effects on the probability that a patient will choose a practice. These are tiny but what matters for the incentive for practices to increase quality is the number of patients they will gain. This depends both on the  effect of quality on the probability of any one patient choosing the practice and the (large) number of patients in whose choice set the practice lies and whose probabilities of choosing the practice are increased. An average practice has 74,529 potential patients aged 25 and over resident within 5 km and 25,070 within 2 km. Column (5) shows that the estimated increase in number of patients in a practice from a 1 SD increase in quality is 917. For comparison, the absolute values of the change in the number of patients from a 1 SD (80 months) increase in GP average age and in the percentage of non‐European qualified GPs (35%) are similar to those for quality, whilst the increase in patient numbers from a 1 SD increase in the percentage of female GPs is about half the size of that of a 1 SD change in quality.Column (6) presents these increases as a proportion of the average number of patients aged 25 and over registered with the practice. This shows the change in patient numbers from a change in quality is important relative to the average number of patients aged 25 and over at just under 17%.We also examine practice cross‐quality elasticities of demand to estimate how many patients a practice will lose if one of its rivals increases its quality. (Details of the computations are in Appendix B.) The larger the cross‐quality elasticities, the more likely is it that qualities are strategic substitutes and that in the long run the equilibrium quality will rise as other practices will respond by increasing their quality thereby increasing equilibrium quality. Here we only estimate the short‐run responses, that is we assume that practices do not respond if they lose patients.The average cross‐practice quality elasticity is −0.044. But there is considerable variation in the cross‐quality elasticities and some cross‐quality elasticities are relatively large. Figure Finally, we illustrate the importance of quality in determining practice lists by using our baseline model to estimate the number of patients a practice would have if patients had no information about quality .6The issue of whether choice and competition will increase the quality of health care services is both current and important. A prerequisite for increased competition to increase quality is that demanders are responsive to quality. We test whether this condition is satisfied in an important setting: the choice by patients of their family physicians. In the context we examine, these physician practices are important as they determine access to both primary and elective hospital health care services at zero direct monetary cost for the patient. Further, this context is not atypical: family doctors are important in many health care systems, they are the first (and often mandated) point of contact with health services and have low or zero direct user cost.We examine the choices of 3.4 million individuals from amongst nearly 1,000 family doctor practices in a region of England. We find that clinical quality is important: individuals are more likely to choose practices with higher measured and published quality. They trade‐off quality against distance. The results are robust to alternative estimation methods, to the way in which distance is assumed to affect choice of practice and to possible restrictions on choice sets through the imposition of catchment areas and practices closing their lists for short periods to new patients.et al.  care less about quality than other groups. We also find that those who live in small areas which are more deprived, or have less healthy or less educated populations, care less about quality relative to distance than others. But the size of the differences in the trade‐offs between quality and distance between these and other groups is relatively small. There was also little difference between those in areas with low and high proportions of the population of Asian origin in willingness to trade‐off quality for distance. This is in contrast to findings for some other services. Schneider et al. , 2000 anet al.  report s  number of individuals who could potentially choose the practice. Using the most conservative of our model specifications, we estimate that a 1 SD increase in measured clinical quality would, in the long run and with no response by rivals, attract approximately 17% more patients to a family practice. Even in the short run this implies large financial rewards as around 75% of practice income is linked to the number of individuals who register with the practice. We also show that, relative to a position with no quality information, practices that gain from the publication of quality are those with higher clinical quality. Finally, our estimated cross‐elasticities suggest that these primary care markets are very local: competition for practices comes from a small number of rivals located within a short distance.Our findings support the argument that the promotion of greater choice of family doctor and provision of information about quality have the potential to benefit patients by increasing quality. Provided the marginal revenue from an additional patient sufficiently exceeds marginal cost, greater competition in this market means that family doctors have an incentive to improve their quality to attract patients. The similarity between patient groups in terms of trade‐offs between clinical quality and distance indicates that such a policy would benefit individuals across all age, gender, morbidity and socio‐economic status groups but would particularly benefit those who currently use practices of low clinical quality.Data S1.Click here for additional data file. Click here for additional data file.


UK. We undertook a retrospective analysis using the Clinical Practice Research Datalink. Patients aged over 40 with full blood count data were risk‐stratified and followed up for a diagnosis of colorectal cancer over a range of time intervals. The primary outcome was the area under the receiver operating characteristic curve for the 18–24‐month interval. We also undertook a case–control analysis , and a cohort study of patients undergoing full blood count testing during 2012, to estimate predictive values. We included 2,550,119 patients. The area under the curve for the 18–24‐month interval was 0.776 . Performance improves as the time interval reduces. The area under the curve for the age‐matched case–control analysis was 0.583 . For the population risk‐scored in 2012, the positive predictive value at 99.5% specificity was 8.8% with negative predictive value 99.6%. The algorithm offers an additional means of identifying risk of colorectal cancer, and could support other approaches to early detection, including screening and active case finding.Earlier detection of colorectal cancer greatly improves prognosis, largely through surgical excision of neoplastic polyps. These include benign adenomas which can transform over time to malignant adenocarcinomas. This progression may be associated with changes in full blood count indices. An existing risk algorithm derived in Israel stratifies individuals according to colorectal cancer risk using full blood count data, but has not been validated in the  Colorectal cancer is the fourth commonest cancer in the UK with 41,265 new cases in 2014, representing 11% of all new cancer Most cases develop slowly over a number of years from benign adenomatous polyps which transform to malignant adenocarcinomas. The identification of the early stages offers the opportunity to improve outcomes through surgical excision Routinely collected primary care data have been used to develop a range of algorithms for identifying individuals at risk of various types of cancers Medial EarlySign, Israel, is developing a range of machine learning algorithmic tools to identify high‐risk patients in various settings . The algorithms are developed by analyzing large complex datasets of millions of patients. Recently, their group reported a study on an Israeli population of two million patients The Israeli model was trained on a derivation dataset and then tested on validation datasets, one from the same source, the other from a sample of UK data from The Health Improvement Network (THIN) The aim of this current study is to evaluate this risk algorithm independently using Clinical Practice Research Datalink (CPRD) data from the UK.. The end date was the date of the most recent update of the CPRD dataset, which may differ between contributing practices. All patients included in the study had at least one FBC present in the record. Entry to the cohort was at the index date, which is defined as the latest of start date, the 40th birthday, or date of registration with the general practice.We undertook a retrospective analysis, following the methods described in Kinar et al., in a population of patients from the CPRD, a large database linked to anonymized patient‐level data from over 600 UK general practices. Study participants were those registered on the database between 01 January 2000 and 28 April 2015Less than 12 months registered with the general practiceLess than 2 years of follow‐up data following the index dateHistory of colorectal cancer before the index dateColorectal cancer precursors Hemoglobin gene defects We excluded the following groups:We included all other patient records, to maximize the precision of our estimates.The primary endpoint is a first diagnosis of colorectal cancer in the primary care record. Patients with no diagnosis were censored at the earliest date of: date of death, the end date or date of leaving the general practice.We extracted the year of birth and sex of each participant, every FBC, each of which could have a maximum of 20 indices, and the date of each FBC.The calculation of the risk score uses the patient identity code, year of birth, sex, and date of FBC. The algorithm requires up to 20 indices of a single FBC, which are imputed if missing. Previous values for the FBC indices are utilized if available but only one FBC is required to generate a risk score. Input files were prepared by the Oxford team, excluding the outcome data. A representative from the Medial team ran the algorithm and returned a dataset containing a score, between 0 and 100, for every FBC considered valid according to their criteria, together with the date of the FBC and the patient identity code.Kinar et al. We identified the FBCs at least 18 months prior to the end date for those with no diagnosis (noncases) of colorectal cancer. For each patient, one FBC and associated score was randomly selected using a computer algorithm. For those with a diagnosis (cases), all FBCs within 18–24 months prior to date of diagnosis were identified. If a patient had more than one FBC within the time period, one FBC was randomly selected using a computer algorithm. Logistic regression analysis was performed with the score as the only predictor in the model. To evaluate the performance of the model, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) calculated (C‐statistic). To provide statistics which could be compared to those reported by Kinar et al., the specificity was calculated when sensitivity was 50%, and sensitivity calculated when specificity was 99.5%.For the primary analysis, we followed the methods outlined in the paper reporting the original derivation and validation of the score by Kinar et al. study, whose primary analysis was based on the 3–6‐month time interval prior to diagnosis, we chose 18–24 months for our primary analysis. This was because the opportunity to modify prognosis requires an adequate time interval for intervention. For our secondary analyses, the performance of the algorithm was measured at 3–6, 6–12, 12–18, and 24–36 months before diagnosis using the same method.In contrast to the To test whether the outcomes were robust to variation in the randomly selected FBCs, the analysis was repeated 10 times, resampling the dataset using a different seed code.As a further sensitivity analysis, a case–control study was designed by matching cases and noncases by sex, year of birth, and year of selected FBC. This was undertaken because of the (expected) difference in the age distributions of the cases and noncases populations.For those with no diagnosis (noncases) we identified the FBCs at least 18 months prior to the end date. For those with a diagnosis (cases), all FBCs within 18–24 months prior to date of diagnosis were identified. If a patient, cases and noncases, had more than one FBC within the time period, one FBC was randomly selected. For each case 100 noncases were selected matching for sex, age at time of FBC, and year of risk score. Logistic regression analysis was performed with the score as the only predictor in the model. The AUC for the receiver operating characteristic (ROC) curve was calculated (C‐statistic).For the analyses set out above, estimation of predictive values  is not appropriate, as there is no defined population to which they would be applicable. It is possible to define a cohort of patients at the start date of the study, 01 January 2000, but the concept of a well‐defined cohort is lost as each patient contributes data at a date set by the randomly selected score. To investigate the result of applying the score to a defined cohort of patients presenting in primary care, we followed until end date all patients with a FBC and score in 2012 who had not previously been diagnosed with colorectal cancer. The year 2012 was chosen as this was the most recent year for which 24 months of follow‐up were available.All patients were followed until they were either diagnosed with colorectal cancer or censored at end date. We conducted a logistic regression analysis with a diagnosis within 24 months as the outcome and the risk score as predictor. Patients who were censored within 2 years of the score in 2012, either because they had died without a diagnosis or were lost to follow‐up, were omitted from the analysis. The AUC for the ROC curve was calculated (C‐statistic). We also calculated values for specificity when sensitivity was 50%, sensitivity when specificity was 99.5%, positive predictive value (PPV), and negative predictive value (NPV).We could not assess agreement between observed outcomes and predicted outcomes, that is calibration, as the risk score does not provide a measure of absolute risk.The project was approved by the Independent Scientific Advisory Committee (ISAC) for CPRD (protocol 14_195RMn) and the protocol was made available to the reviewers.We identified 2,914,589 patients who met the inclusion criteria and had at least one FBC. The mean number of indices comprising a FBC was 11.8. The percentage count of neutrophils, monocytes, eosinophils, basophils, and lymphocytes (components of white blood cells) were recorded for only 10% of FBCs but these can be derived from the cell counts. The mean cell volume, mean cell hemoglobin and mean cell hemoglobin concentration can also be derived from values of other indices. This increased the mean number of indices available to 15.6. Those most frequently missing were mean platelet volume (present in 14% of FBCs) and red cell distribution width (present in 2% of FBCs). The Table Data were excluded when more than one FBC belonging to an individual patient was associated with a single date or when hemoglobin was missing from the FBC. A total of 364,470 patients were excluded as they had insufficient FBC indices to generate a score. The total number of patient years of data from the remaining 2,550,119 patients was 27,949,304 indicating that the average time in the study was 11.0 years. The mean age at the start date was 54.2 years, and 55.4% of the patients were female.For the included patients, we identified 25,430 with an incident diagnosis of colorectal cancer. We excluded 68 patients whose age at diagnosis was less than 40 years. The mean age of diagnosis was 71.2 years. 20,492 cases had at least one FBC before diagnosis. The time of the closest FBC to diagnosis varied between 0 and 5580 days. A total of 7634 cases had a FBC within 2 years before diagnosis.We identified 2,220,108 patients without a diagnosis who had at least one FBC and related score at least 18 months before end date, and 5141 patients with a diagnosis who had at least one FBC and related score within the time window of 18 to 24 months before diagnosis. For each patient that met the inclusion criteria, one FBC and associated score was randomly selected. The mean age (standard deviation) of those without a diagnosis was 60.5 years (14.0) and of those with a diagnosis, 72.7 years (10.5). The mean age at time of the FBC and the mean score by sex and colorectal cancer diagnosis are reported in Table The results of the logistic regression analysis with the score as the only predictor are reported in Table Table The resampling exercise confirmed that the analyses were robust to variation in the randomly selected seed codes. The primary outcome AUC gave a narrow range of values in the 10 samples (0.7755 to 0.7765) around a mean of 0.7760. The risk score cut off associated with 99.5% specificity was identical (99.78) for all ten.Tables For the estimation of predictive values, we identified 797,119 patients with a score measured in 2012, and with no diagnosis of colorectal cancer at the time of this score. Male and female patients were pooled, age range 40‐108 years. Patients without 2 years of follow‐up from score were excluded. Within 2 years 36,032 4.5%) of the patients had died and 160,814 (20%) were lost to follow‐up. The mean age and score of these groups are reported in Table % of the Kinar et al.Assuming that the patients included in the analysis reported in Table Kinar et al. for their primary analysis, whose AUC was 0.82. In our study, this shorter time interval gave a very similar value of 0.84, validating the risk algorithm performance in the UK. Most of the predictive power is due to age, as is evidenced by the reduction in AUC to 0.583 in the case–control sensitivity analysis, which removes the age component through age matching. However, age is indeed a useful factor in determining colorectal cancer risk, and the addition of FBC indices to age and sex improves the ability to identify patients at risk.The risk score applied to routinely collected primary care data from the UK produced AUC values comparable with those from the Israeli population used to derive it. Our primary outcome interval (18–24 months) produced an AUC of 0.776. This interval was chosen as it provides a greater opportunity to intervene and modify prognosis than the 3–6‐month interval chosen by Kinar et al. in the Israeli setting.After removing our excluded groups, our primary analysis only included 5141 of 25,430 colorectal cancer patients in the dataset. The FBC data from the Israeli population consist of more indices than are found in the UK CPRD data. Patients in Israel have full blood counts taken routinely as part of regular health checks, while in the UK, the test is usually conducted for some clinical reason. The fact that a blood test has been requested therefore carries some predictive weight itself in the UK setting, although FBCs are requested for a wide variety of reasons, most unrelated to colorectal cancer, and our results were similar to those found by Other known risk factors for colorectal cancer, such as family history and microsatellite instability (MSI) status are not included in this approach. MSI status is not usually available in the primary care setting, and family history is recorded inconsistently. One of the advantages of this risk algorithm is that provided an individual has had a full blood count taken, the values of all the indices should be available, with very little missing data.Absolute risk is assumed to be a monotonic function of risk score, but the form of the function is unknown. Discrimination can be investigated using sensitivity, specificity, and the area under the ROC curve, but we cannot easily quantify how close the predictions of risk are to actual risk.The outcomes recorded in CPRD and used in this study are incident diagnoses of colorectal cancer following the risk estimation, which is different from the prevalence of undiagnosed cancer in those identified. This prevalence could only be measured through a study using the gold standard on the population at risk. Some of the individuals with no diagnosis during our study may be diagnosed after a longer time interval than we were able to follow‐up. The detection of precancerous lesions is also very useful clinically as it enables prevention of future cancer and this outcome was not included in this study.Kinar et al. Following their method, we have reported values for risk score cut offs corresponding to a sensitivity of 50% (≥83.47) and for a specificity of 99.5% (≥99.78). Kinar et al. undertook further analyses based on stage of cancer diagnoses. These were not available for our analyses in CPRD but we intend to carry out further analyses through linkage to the Cancer Registry. Kinar et al. derived measures of absolute risk for patients identified, but for our study these were not available. We attempted this through a sensitivity analysis and derived an estimate of the PPV of 8.8% for the high‐risk score cut off. However, identifying a meaningful “cohort” population in retrospective studies such as these is problematic.Our analysis was designed to validate independently the results obtained by Comparing our results with those of Hippisley‐Cox and Coupland Application of the Medial algorithm to routinely collected primary care data offers a potential additional means of identifying those at risk of colorectal cancer. For a patient with a risk score value associated with 99.5% specificity, we estimate a risk of being diagnosed over the next 2 years of 8.8%. This outcome does not include additional patients who have a precursor lesion (adenoma), who will also benefit from early recognition.As expected, there is a trade‐off between algorithmic performance  and opportunity to intervene . Confirming the diagnosis in an individual found to be at risk usually involves invasive procedures such as colonoscopy, which is not itself without risk although serious complications are rare These findings may provide a role for the risk score as a tool to assist case finding in a range of settings. The score could be applied to primary care electronic health records and be automatically updated each time a FBC result appeared in the record. Patients with high (or increasing) values could be identified and considered for investigation. However, this high specificity approach will inevitably be limited in its ability to capture cases. At the less extreme cut off values, where sensitivity is higher, the algorithm could also help target those who do not take up the invitations for FOBt or who refuse colonoscopy when offered through the screening program.Our results need to be followed up through further research investigating the utility of the risk algorithm embedded in the routine care setting, to optimize the early detection of colorectal cancer and improve health outcomes. This would involve measuring the risk scores of a greater proportion of the at risk population, and investigating pragmatically the additional benefit offered to early detection of bowel cancer.This is granted for all CPRD studies approved by the Independent Scientific Advisory Committee. No further study‐specific approval is required.None declared.Table S1. Percentage of missing values for each blood level in the dataset of full blood counts retrieved from CPRD.Click here for additional data file.


Poor malaria knowledge can negatively impact malaria control programmes. This study evaluates knowledge distribution in the domains of causation, transmission, vulnerability, symptoms, and treatment of malaria. It assesses the association between a caregiver’s knowledge about malaria and ownership and use of insecticide-treated nets (ITNs) by children.Some 1939 caregivers of young children were recruited through a school-based survey in two Nigerian states. A 20-item, multi-dimensional survey instrument was developed and used to rank each caregiver’s knowledge in five dimensions . Scores for each domain were used to create an aggregate knowledge score for each caregiver. The outcome measures were ITN ownership, and ITN use the night and week before the study. Regression models were used to evaluate the relationship between caregiver’s knowledge  and ownership and use of ITN after controlling for likely confounders.The main predictor of ITN use was ITN ownership ; however, ownership only explains 43 % of variance in net use. Total knowledge index for the study population was significantly associated with both ITN ownership  and use . The spectrum of caregiver’s knowledge of malaria and its causes captured in the various domains was, however, found to be poor. Fifty percent of the respondents knew that malaria is transmitted by female mosquitoes and 65 % still believe that too much exposure to the sun is a risk factor for malaria. Knowledge of populations most vulnerable to malaria (83 %) and knowledge of malaria transmission (32 %) were the domains with the highest and lowest average correct answers.There is a need to improve ITN coverage in Nigeria as ITN ownership was associated with ITN use. Additionally, treating knowledge as a multi-dimensional phenomenon revealed that a lot of misperceptions about malaria still exist. Distribution of ITNs through the public/private sector may need to be augmented with tailored behavioural change communication to dispel myths and improve the multi-dimensional knowledge of malaria in the local population. Malaria may be conceptually simple to eradicate, but operationally complex. In theory, all it takes to stop mosquitoes from transmitting malaria parasites is a physical barrier  between the human host and mosquito and a course of pills that costs pennies to reduce the reservoir of infections in human carriers , 2. ThisIn this study, malaria knowledge is regarded as a multidimensional phenomenon determined by the interplay of five factors (or dimensions): cause of the disease; transmission or how the disease is spread; effective prevention strategies; current treatment regimen; and, vulnerability to the disease –9. Each Nigeria, which has the highest number of childhood deaths from malaria in Africa , has useThis study reports a multidimensional survey instrument developed to assess malaria knowledge  as predictors of ownership and use of ITN in Nigeria. Specifically, the questions being asked are: (1) How correct is the malaria knowledge in the country? (2) Does high correct malaria knowledge predict ownership of ITNs? (3) Does high correct malaria knowledge of caregivers translate into increased use of ITNs to protect children? The results of this study can be used to enhance the effectiveness of educational interventions in current global efforts to eradicate malaria .This was a community-based survey of parents where the sampling frame was children attending primary schools. Young children were the population of interest because they are susceptible to malaria and primary schools were a convenient setting to recruit study subjects with low cost and high efficiency. A pre-piloted, self-administered questionnaire was developed for collection of data . The quePlasmodium falciparum [Anopheles gambiae [Four local government areas (LGAs) in each of the states of Lagos and Oyo in southwest Nigeria were selected based on whether they had participated in ITN distribution campaigns . Malarialciparum  with the gambiae . The surA list of accredited primary schools in the selected LGAs was received from the Ministries of Education in Lagos and Oyo. Fifteen public and 21 private primary schools were chosen for their accessibility. Children in Grades 1 to 3 in each selected school were given a questionnaire to take home to their caregivers. Where a family had more than one child in the target grades, only one of the children was allowed to participate in the study. To achieve a margin of error of 3 % with 95 % confidence interval and assuming 50 % ITN ownership in each state, the final sample size was calculated to be 1200 caregivers/children. There were 2400 questionnaires given out presuming a minimum participation rate of 50 %. Out of the 2400 questionnaires handed out, 1939 were returned (representing a capture rate of 81 %); 47 of these had a lot of missing data and were excluded in the analysis of the knowledge domains.The project was determined to be exempt from institutional review board (IRB) by the University of Michigan Health Sciences and Behavioral Sciences IRB since participants were not asked to provide any identifying information . Anonymizing the data collection was a way of ensuring the ‘truthfulness’ of the answers. In both Lagos and Oyo States, permission to involve children in the selected primary schools in the chosen LGAs was obtained from the appropriate ministries.All data were entered and cleaned using Microsoft Access and analysed using SPSS version 20. Descriptive statistics were computed for all relevant data. Quantitative variables were summarized using mean, standard deviation and range while frequency tables were created for categorical variables. Knowledge indices were calculated as the sum of the scores for individual items in each sub-scale (domain knowledge index) and for all the items as a whole . Analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) were conducted.The malaria knowledge statements were made up of correct and incorrect statements. The answers were recoded so that respondents who agreed or disagreed with an incorrect statement were given a ‘0’ and ‘1’, respectively. Respondents who agreed or disagreed with a correct statement were given a ‘1’ and ‘0’, respectively. ‘Don’t know’ and no answer responses were treated as incorrect and coded as a ‘0’. Knowledge scores  were then created according to the following formula:This was done so that the knowledge score ranged theoretically from 0 to 100 % and reflected percent of knowledge items answered correctly. Hence, higher scores reflected more correct total malaria knowledge. For most of the statistical analysis, each knowledge score was divided into two categories: higher than the mean score and less than or equal to the mean score. Bivariate analyses of the score and sociodemographic variables were conducted using Chi square tests. A p value of 0.05 or less was considered significant.Binary logistic regression was used to evaluate the association between each malaria knowledge score and ownership of an ITN. Among ITN owners, multinomial logistic regression was used to assess the use of an ITN by children in the sample the week before the survey with respect to their caregiver’s malaria knowledge scores. Caregivers with scores greater than the mean were compared against caregivers with scores lower than or equal to the mean. All binary and multinomial logistic regression models were adjusted for the following variables: state of residence, gender and age of both child and caregiver, educational level and income range of caregiver. Season and state of residence were automatically correlated hence state of residence was controlled for since only one variable could be used in the models.The survey instrument was found to have good psychometric properties. The Cronbach’s alpha was 0.725 considered to indicate good internal consistency (‘reliability’) for such instruments . The alptoo much exposure to the sun causes malaria”. Knowledge in the domain of malaria transmission was poor since less than half of the respondents (47 %) knew that malaria could be transmitted during the dry season. The domain with the highest correct answers was Vulnerability. Average correct score of each of the three items in this domain was 80 % or over ) for the survey instrument was 41.6 ± 6.7, which is slightly higher than half the maximum possible score of 80. The average scores for the sub-scales were 6.8 ± 1.6 for Transmission, 4.9 ± 1.5 for Vulnerability, 9.9 ± 2.5 for Symptoms, and 7.6 ± 1.8 for Treatment. The relationships between ITN ownership, ITN use, TKI, and individual domain scores are shown in Table 2 value was 0.205), the associations found in this study suggest that the domains are indeed measuring different elements of knowledge within the study population.The strongest explanatory factor for the use of ITNs to protect children was ownership of the net  and ownership of any net for that matter  (Table 2 = 0.032) and malaria prevention . The knowledge domain with significant main effects on ITN use were malaria cause ; malaria prevention ; and, malaria transmission .Results from the MANOVA showed that the five knowledge domains were significant influencing factors on both ITN ownership  and ITN use . However, only a very small fraction of the variance in ITN ownership (2.3 %) and use (2.8 %) could be explained by the knowledge measures. The univariate test results (ANOVAs) for each knowledge domain show that the significant latent variables were related to causes of malaria  was associated with ITN ownership and use. Results of the MANOVA showed that the knowledge domains with significant main effects were malaria cause and malaria transmission. The knowledge measures only explained a small fraction of the variance in ITN ownership and use. These results suggest that other factors may be playing a role in ITN ownership and use in this population and highlight the importance of understanding the local context before interventions are conducted. This may explain the discrepancy between the policy assumption that knowledge enhances the ownership and use of ITNs and what is being observed in the field. Some studies have reported no correlation between malaria knowledge and the use of bed nets, including ITNs , 22, 32.ITN ownership was a determining factor in a child using the ITN in this study. This finding is similar to results from other studies evaluating predictors of ITN use , 37. In However, ITN ownership does not always translate directly to ITN use. Factors such as correct knowledge are particularly important to enable the large proportion of the participants using malaria control strategies to assess the risks and benefits of the methods available to them , 32. TheKnowledge of malaria cause was poor with the average score for correct answers being 44 % Fig. . The scoCulex mosquitoes which have a different biting pattern from Anopheles mosquitoes that are mostly active from dusk until dawn [Knowledge of malaria transmission had the lowest correct answers with a mean score of approximately 32 % Fig. . The majtil dawn . This fatil dawn , 46. Alstil dawn . NeverthVulnerability to malaria was the domain that had the highest score showing that caregivers are cognizant of the risk of malaria. This should not be surprising since malaria is a common chronic health problem in Nigeria and people generally have a relatively high degree of understanding about its antecedent cause (mosquitoes) as well as know that anyone is at risk of malaria regardless of age. It may also be related to risk communication that stresses the effectiveness and benefits of net-centred vector control methods almost to the exclusion of other malaria control strategies. While results showed that vulnerability was associated with partial use of an ITN, an interesting question is why this domain did not promote behaviour change towards consistent ITN use by children. One suggestion is that this domain is phenomenally linked to local risk perception: a mixed dose of emotion, experience, belief, and knowledge , reporteKnowledge of common malaria symptoms was average (slightly over 50 % of the answers were correct), with the majority of respondents knowing that fever and vomiting are associated with the disease Fig. . The low®, which is one of the most popular artemisinin-based combination therapy (ACT) drugs being used in Nigeria, is effective against malaria  knew that malaria needed to be treated immediately, the overall score of correct answers for malaria treatment was 50 %. This finding suggests that most people are still not sure what to use for treatment of childhood malaria in the country. In this study, over 30 % of caregivers believed that traditional herbs/medicine are a good way to treat malaria. Similar findings from northeastern Nigeria and Côte d’Ivoire show that paradoxically, people with sound knowledge of malaria cause and symptoms are still likely to use traditional medicine . Anotherria Fig. . This stACT is recommended by the WHO as first-line anti-malarial treatment and was adopted in Nigeria in 2004 . Based oThere are limitations in this study that should be noted. First, the study was based on self-interview and reported ownership and use of ITNs was not validated with actual observation. Second, the cross-sectional nature of this study is limited in its ability to establish a cause and effect relationship between predictors and outcomes. Third, the information collected on ITN use was based on a recall period of the week preceding the survey  so the data could be subject to recall bias and social desirability bias where caregivers might have reported more use by children than their actual use. Lastly, these data are based on children who were actually present in school the day the survey was passed out, hence some of the target children might not have been sampled. Notwithstanding these limitations, this study adds a different perspective to current knowledge about malaria in two Nigerian states and the potential implications for malaria control in the country.This study documents a lot of misperceptions about malaria among the Nigerian caregivers surveyed in the two states. Few of the respondents got high scores across all domains of the measurement instrument used, indicating that correct knowledge about malaria is very limited in the two states surveyed. Study data shows that ownership of ITNs was a stronger predictor of ITN use when compared to total knowledge. However, specific knowledge domains were associated with ITN use. Additionally, evidence from other studies shows that ownership does not automatically translate to use. It is definitely important to increase ITN coverage and a lot of resources are currently being invested in trying to achieve this goal especially in malaria endemic countries like Nigeria. However, educating the local populace has also been shown to be important. While efforts to improve ITN coverage occur, local knowledge of different domains of malaria knowledge may need to be measured as an adjunct to efforts to develop tailored and effective educational interventions needed in the current global effort to eradicate malaria. While knowledge is just one of a complex interplay of factors that drive malaria-related behaviours, it affects attitudes towards malaria control and is an important prerequisite for influencing behaviour change.


Young’s modulus and Poisson’s ratio have been measured simultaneously on a series of particulate composites containing volume fractions of filler up to 0.50. The composites consisted of small glass spheres imbedded in a rigid epoxy polymer matrix. The measured values were compared with theoretical values calculated from current theories. A recently generalized and simplified version of van der Poel’s theory provided the best agreement. It predicted values of Young’s modulus for composites with filler volume fractions up to 0.35. Measured values of Poisson’s ratio exhibited scattering, but were consistent with values calculated from van der Poel’s theory. It has been known for some time that the addition of a particulate filler to a polymeric material can greatly affect the elastic properties of the resulting composite. The effect is a mechanical one complicated by several superimposed effects of a chemical and physical nature. The pure mechanical effect is best understood. It is accounted for in terms of the local strain distortions due to dissimilarities between the matrix and filler material. Various theories have been propounded to explain it, and reviews of these theories are available –31.In order to test these theories good data are needed for a series of macroscopically homogeneous and isotropic composites containing various known amounts of filler. The filler and matrix materials should be isotropic, and the two elastic constants necessary to characterize each of them should be known. These two elastic constants should preferably be obtained by simultaneous measurements on the same specimen in order to minimize effects due to viscoelasticity and specimen variation.There are a number of physical and chemical effects that must be considered in choosing an appropriate composite system for testing. The rubbercarbon black system for instance is complicated by the following effects –6: The sIn some cases the presence of the filler particle may induce crystallization, crosslinking or other structural changes in the surrounding shell of polymer matrix, thus introducing an additional phase with consequent changes in mechanical properties –11. ThisThe room temperature behavior of a composite formed at elevated temperatures may be affected by frozen-in stresses due to differences in the thermal expansion properties of the matrix and filler material . In castThe shape and orientation of the filler particles may cause the composition to be anisotropic, hut this effect is easily avoided by using spherical filler particles.Previous checks of the theories have met some of the above criteria in various ways. For those composites consisting of a rigid filler imbedded in a rubbery matrix of Poisson’s ratio 0.5, the shear modulus of the composite increases as the volume fraction of the filler is increased. This effect is analogous to the increase in viscosity of a suspension of particles in a liquid , so thatData are also available on composites with a rubbery matrix, but these data are usually incomplete in that only one elastic constant is measured as a function of the volume fraction of filler. In these cases the dependence on filler content of the other constant, that is needed to characterize an isotropic material, cannot be checked. Schwarzl et al.  and WateWhen the polymer matrix is in the leathery or glassy state, one can no longer assume as in the rubbery case that Poisson’s ratio of the matrix is close to 0.5. Yet, unless two elastic constants are known for the matrix material, and reasonable values of the constants can be assumed for the filler, theoretical predictions for the composite’s elastic behavior can only be approximate. In much of the data for glassy-matrix composites the constants of the components are not completely specified, and in most cases only one constant characterizing the composite (i.e. Young’s modulus or shear modulus) is measured as a function of filler content. Some useful but usually incomplete data are available , 19–22. The system of composites studied consisted of glass spheres imbedded in and adhering to a glassy epoxy matrix. A series of such materials was fabricated, each member containing a different volume fraction of glass spheres.3 for material stored 185 days at 24 °C and 54 percent R.H.The matrix material was formed from the diglycidyl ether of bisphenol-A (DGEBA) hardened with a stoichiometric amount of triethylenetetramine (TETA). The DGEBA resin used μm. The glass was an optical crown glass, soda lime type, with a silica content not less than 60 percent . The manufacturer specifies an approximate Young’s modulus for this glass of 7.6 × 1010 Pa (11 × 106 lb/in2) as measured on a bulk sample. Poisson’s ratio for soda lime glass, given in the literature [3.The filler material consisted of glass spheres with a distribution of diameters in the range 1 to 30 terature , is 0.23γ-aminopropyltriethoxysilane  as follows: A given weight of spheres was added to an equal weight of freshly prepared 1 percent-solution of A1100 in water. The slurry was stirred for 15 min, filtered, and washed with an equal amount of water. The spheres were then dried overnight under vacuum at 130 °C, and then lightly ground in a mortar to break up agglomerations.Before use the glass spheres were cleaned by passing them near the poles of a powerful permanent magnet a number of times to remove iron impurities. They were washed twice in boiling distilled water and twice in boiling isopropanol, and dried overnight under vacuum at 130 °C. The dried spheres were coated with the coupling agent Before use the DGEBA monomer was deaerated in a vacuum oven for at least 1 hour at 60 °C. A weighed amount was transferred to a flask, the glass spheres added, and the slurry stirred under vacuum for an hour to remove air introduced with the spheres. The TETA hardener was added, and the mixture rapidly stirred under vacuum for 5 minutes. The mixture then was carefully poured into a mold formed from clamped glass plates separated by spacers and sealed with rubber tubing. The plates had previously been treated with a mold release agent.The mold was sealed and the comems allowed to cure for 16 hours at room temperature. Before gelling occurred the mold was rotated at 1 rpm in order to prevent the glass spheres from settling. The material was post cured in an unclamped mold for 24 hours at 75 °C followed by 8 hours at 150 °C. The oven was then turned off and the cured composite allowed to come to room temperature overnight in the oven. According to reported research on similar materials  this shoSeveral series of composites containing volume fractions of glass spheres up to 0.50 were prepared by this process. The content of glass was determined by hydrostatic weighing and found to be uniform throughout each of the cast samples. Test specimens of dumbbell shape  with 5-cThe specimens were tested on a tensile testing machine at 0.05-cm/min rate of extension and 12.5-cm grip separation. The state of strain in the uniform narrow portion of the specimen was monitored with a strain gage extensometer of nominal 5-cm gage length. The exact value of the initial gage length was determined in situ with a cathetometer. A transverse extensometer employing a small linear variable differential transformer was used to obtain simultaneous measurements of the width of the specimen during test. The amplified outputs of the two extensometers and the load-extension curve of the tensile tester were recorded separately. Simultaneous data values were obtained by putting small simultaneous pip marks on the recorder traces by means of a pushbutton operated pulse circuit.The data were used to plot stress-strain curves and curves of transverse strain versus longitudinal strain for the specimens. The initial slope of the stress-strain curve was taken as Young’s modulus of the specimen, and the negative of the initial slope of the transverse strain-longitudinal strain curve was taken as Poisson’s ratio. As all of the specimens were subjected to the same low rate of straining during data acquisition, it was believed that the relative effects of viscoelasticity would be small. Therefore viscoelastic effects were not considered in the subsequent analysis.During early stages of the research it was assumed that the samples were fully cured, so that tensile properties could be measured 1 or 2 weeks after preparation. The results of these measurements are given in 3)2 · 6H2O maintained at the laboratory temperature of 24 °C. This provided an environment of 54 percent relative humidity. As a check on the effect of moisture two extra samples of epoxy matrix material were prepared. One was stored over water, and the other was stored over anhydrous CaSO4 in a vacuum desiccator.A later series of samples made and measured is listed in Comparison of the values given in G represent the shear modulus, K bulk modulus, E Young’s modulus, and ν Poisson’s ratio of the composite; let the subscripts f and m refer to the filler and matrix respectively, and let φ represent the volume fraction of filler. These elastic constants are interrelated by the equations,The data just presented will now be analyzed with the help of a theory due to van der Poel , and comG has been simplified [Relation mplified .mE, mν, fE, fν of the matrix and filler materials are known, the corresponding values m, GmK, fK, fG can be calculated by means of relations K and G for the composite can be calculated as a function of φ, using E and ν then can be calculated from relations E and ν that are compared with the experimental data.If the elastic constants mE and mν for use in the theoretical calculations. The average of the measured values of mE and mν was not regarded as sufficiently accurate, so more accurate values were obtained by the following extrapolation procedure: The averages of mE and mν were used as a first approximation, and curves of E versus φ and ν versus φ were calculated using van der Poel’s theory. These curves were fitted to the experimental data for values of φ up to 0.15. This was done by shifting the curves mathematically along the E or ν axis to obtain a least squares fit with the experimental data. The shape and slope of the curves was maintained constant during the shifting process. The intercepts of the shifted curves provided new values of mE and mν. The process was repeated until convergence was obtained.In order to determine how well the theories agree with the experimental data it is necessary to have accurate values of mE and mν obtained by this process are: For the data of mE = 2.68×109 Pa, mV = 0.394; for data of mE = 3.01 × 109 Pa, mν = 0.394. It is apparent from the above values of mE that age hardening occurred during the time interval between tests at 10 days and 200 days after sample preparation.The values of mE\E for the samples are plotted as a function of filler volume fraction in mE = 2.68 × 109 Pa), and the solid lines show values for the age hardened samples (mE = 3.01 × 109 Pa). Predicted values of Kerner’s widely used theory are the same as those of Hashin and Shtrikman’s highest lower bound H1, and have been shown to be approximations to the values calculated using van der Poel’s theory [Relative moduli s theory .mE/E calculated using van der Poel’s theory are in good agreement with the measured values for filler volume fractions up to 0.35, and give slightly better predictions than values calculated using the Kerner or Hashin and Shtrikman lower bound theory. At higher filler volume fractions up to 0.50 measured values exceed the van der Poel predictions, but are less than those of Budiansky.It is apparent from Poisson’s ratios are plotted in mE/E versus φ. Thus curve H2 calculated using Hashin and Shtrikman’s values for the least upper bounds on K and G actually becomes a lower bound curve for Poisson’s ratio ν, and curve H1 calculated from the highest lower bound values becomes the upper bound curve. Also the curve calculated from Budiansky’s theory lies below that calculated from van der Poel’s theory. The reversal in the order of these curves is a consequence of the decrease in the value of ν with increasing φ.The order of the curves in Although there is considerable scatter in the data, the curve of values calculated from van der Poel’s theory seems to provide the best fit. These results indicate that the generalized van der Poel theory is better than other current theories in predicting the mechanical effect of filler on the elastic properties of a particulate composite. As the calculations involved in the application of this theory have now been simplified , 30, it


A search is presented for massive long-lived particles, in the 20–60  The standard model of particle physics (SM) has shown great success in describing physics processes at very short distances. Nevertheless, open questions remain, such as the hierarchy problem, the imprecise unification of gauge couplings, and the absence of candidates for dark matter. Considerable efforts have been made to address these issues, resulting in a large variety of models. Supersymmetry (SUSY), in which the strong and electroweak forces are unified at a renormalisation scale near the Planck scale, provides a possible solution for the hierarchy problem; the minimal supersymmetric standard model (MSSM) is the simplest, phenomenologically viable realisation of SUSY , 2.The present study focuses on a subset of models featuring massive long-lived particles (LLP) with a measurable flight distance. We concentrate on scenarios in which the LLP decays hadronically in the LHCb vertex detector, travelling distances which can be larger than those of typical A large number of LLP searches have been performed by the experiments at the LHC and Tevatron, mainly using the Hidden Valley framework  as a benpp collisions decays into two LLPs , subsequently decaying into three quarks each. The Higgs-like particle mass ranges from 80 up to 140 The event topology considered in this study is quite different from that of Hidden Valley models. The minimal supergravity model (mSUGRA) realisation of the MSSM is used as a benchmark model with baryon number violation , as suggeriments , 14. Thepp interaction region (VELO), a large-area silicon-strip detector located upstream of a dipole magnet with a bending power of about The LHCb detector , 16 is a trigger , which cpp collisions are generated with Pythia  6 [Pythia. The corresponding decay flavour structure for the neutralino with a mass of udb, usb, cdb, csb), and 13% for each udq and cdq, where q is not a Various simulated event samples are used in this analysis. The ythia  6 . The prom CTEQ6L . The neuGeant4  [Geant4, the detector and its response are implemented as described in Ref. [Geant4, corresponding to about 30% of the generated events.Two separate detector simulations are used, a full simulation where the interaction of the generated particles with the detector is based on Geant4  , 21, and in Ref. . Signal The fast simulation is used to cover a broader parameter space of the theoretical models. Here the charged particles from the Events with direct production of charm, bottom and top quarks are considered as sources of background. Samples of such events were produced and fully simulated. In particular, This analysis searches for events with pairs of displaced high-multiplicity vertices. The main background is due to secondary interactions of particles with the detector material. These events are discarded by a material veto, which rejects vertices in regions occupied by detector material . The remFrom simulation, LLP candidates within the detector acceptance are selected by the L0 and HLT1 triggers with an efficiency of more than 85%. The simulation indicates that the trigger activity is dominated by the hadronic component of the signal expected from high multiplicity events. In HLT2, primary vertices and displaced vertices are reconstructed from charged tracks . GenuinePythia, and the detection efficiency obtained from the simulated events. The measured yield has also been compared to the rate observed in LHCb by a dedicated inclusive The preselection criteria drastically suppress the hadronic background. Only 37 events (74 LLP candidates) survive from the simulated set of ument}μb , 26, preculation  to correz axis, parallel to the beam. The values of The shapes of the distributions of the relevant observables are compatible with the mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimThe reconstructed four-vectors of the two LLPs in the event are added to form the Higgs-like candidate (di-LLP), the corresponding invariant mass and Further cuts are applied to the preselected data, to increase the statistical sensitivity. The figure of merit used is given by mentclass2pt{minimThe signal yield is determined by a fit of the di-LLP invariant mass, assuming that the two LLPs are the decay products of a narrow resonance. This technique is hampered by the difficulty in producing a reliable background model from simulation, despite the fact that it is reasonable to believe that only mentclass2pt{minimThe signal template is the histogram built from BV simulated events selected under the same conditions as data, i.e. entclass1pt{minimaThe behaviour and sensitivity of the procedure is further studied by adding a small number of signal events to the data according to a given signal model. Figure An alternative fit procedure has been applied, using parameterised signal and background templates. The sum of two exponential functions is used for the background, and an exponential convolved with a Gaussian function for the signal. The results are consistent with a null signal for all the models.As a final check a two-dimensional sideband subtraction method (“ABCD method” ) has beeThe determination of the detection efficiency is based on simulated events. The geometrical acceptance for the detection of one The consistency between the trigger efficiency in data and simulation is checked by selecting LLP events with an independent trigger, designed for the detection of The consistency between the track reconstruction efficiency in data and simulation is studied by a comparison of the number of tracks selected in displaced vertices from The vertex reconstruction efficiency is affected by the tracking efficiency and resolution. A study of vertices from A maximum mismatch of 10% on both the transverse momentum and mass scales is inferred from the comparison of data and simulated The effect of the material veto corresponds to a reduction of the geometrical acceptance and depends mainly on the LLP lifetime. An analysis with the requirement of A small contribution to the systematic uncertainty of 0.1% is determined by reweighting the simulated events to match the PV multiplicity in the data.The uncertainty on the position of the beam line is less than 20ment}μm  . It can Pythia generated events to match a recent calculation of the The Higgs-like particle production model is mainly affected by the uncertainty on the parton luminosity. A maximum variation of the detection efficiency of 9.5% is obtained following the prescriptions given in . A seconibutions . The totIn addition to the systematic uncertainty on the detection efficiency, the following contributions have been considered. The uncertainty on the integrated luminosity is 1.7% . As prevFor the analysis based on the fast simulation, a 5% uncertainty is added to account for the relative imprecision of the fast simulation with respect to the full simulation, as explained in Sect. The 95% confidence level CL) upper limits on the production cross-section times branching ratio are presented in Table L upper lThe estimated detection efficiencies can be found in Appendix pp collisions at 7A search for Higgs-like bosons decaying into two long-lived particles decaying hadronically has been carried out using data from The model used to describe the LLP decay is an mSUGRA process in which the lightest neutralino


We report on the results obtained for a cohort of 20 patients. The plan complexity was investigated for each plan using the complexity metrics of homogeneity index, conformity index, modulation complexity score, and the fraction of beams from a particular plan that intersect the chamber when performing the QA. Rotated QA gives more consistent results than the collapsed QA technique. The kQclin,Qfclin,fref factor deviates less from 1 for rotated QA than for collapsed QA. If the homogeneity index is less than 0.05 then the kQclin,Qfclin,fref factor does not deviate from unity by more than 1%. A value this low for the homogeneity index can only be obtained with the rotated QA technique.IMRT QA requires, among other tests, a time‐consuming process of measuring the absorbed dose, at least to a point, in a high‐dose, low‐dose‐gradient region. Some clinics use a technique of measuring this dose with all beams delivered at a single gantry angle (collapsed delivery), as opposed to the beams delivered at the planned gantry angle (rotated delivery). We examined, established, and optimized Monte Carlo simulations of the dosimetry for IMRT verification of treatment plans for these two different delivery modes (collapsed versus rotated). The results of the simulations were compared to the treatment planning system dose calculations for the two delivery modes, as well as to measurements taken. This was done in order to investigate the validity of the use of a collapsed delivery technique for IMRT QA. The BEAMnrc, DOSXYZnrc, and egs_chamber codes were utilized for the Monte Carlo simulations along with the MMCTP system. A number of different plan complexity metrics were also used in the analysis of the dose distributions in a bid to qualify why verification in a collapsed delivery may or may not be optimal for IMRT QA. Following the Alfonso et al.PACS number: 87.55.Qr I.In order for intensity‐modulated radiotherapy (IMRT) treatments to achieve their potential, it is necessary to be able to accurately verify the radiation dose that will be administered to the patient in these treatment techniques. Previous studies have shown that standard treatment planning methods in certain clinical IMRT configurations are limited in the prediction of the radiation dose. For example, Ibbott et al.There are many reports documenting the limitations of commercial treatment planning algorithms in planning IMRT treatments, especially in complicated situations such as for head and neck cancers.1)measurement of the absorbed dose to a point in a high‐dose, low‐dose‐gradient region in a clinically substantial volume of the phantom with an ion chamber; and2)the measurement of the two‐dimensional relative dose distribution, with either film, a diode array device, or an ion chamber array device, and a gamma analysisOne of the primary issues with a more widespread implementation of intensity‐modulated radiation therapy (IMRT) in clinics with the necessary equipment is that it can be substantially more time‐consuming than conventional radiotherapy. Work by Miles et al.As this procedure is required for each IMRT patient plan before treatment commences, it can be seen how this would contribute to a substantial increase in time and personnel requirements. The measurement of the relative dose distributions is discussed in more detail in the work by Nelms et al.A recent survey by Nelms and Simon10×10 cm2 field.Alfonso et al.kQclin,Qfclin,fref factor for collapsed and rotated beam deliveries. The investigation looks at different plan complexity metrics to determine a predictor of the reliability of the QA technique. The relationship between the kQclin,Qfclin,fref factor and the dose homogeneity index investigated could help with determining suitable reference fields (PCSR fields) when implementing the Alfonso et al.10×10 cm2). This would help clinics perform more accurate QA on IMRT plans, quantitatively compare results, and ultimately improve the reliability of patient treatments.This work aims to use Monte Carlo methods to investigate the reliability of using a single gantry angle or collapsed beam configuration, as opposed to measuring the doses at the planned angles or rotated beam delivery, when measuring the absorbed dose to a point in a high‐dose, low‐dose‐gradient region.II.MMCTP is a radiotherapy research platform, which enables comparison and analysis of dose distributions from both treatment planning systems and quality assurance measurements, with values calculated with Monte Carlo simulations on a common independent platform.The MMCTP systemOnce an accurate linear accelerator model has been commissioned, the BEAMnrcegs++The EGSnrcA.30×30×17 cm3 Solid Water phantom , with the chamber at isocenter and perpendicular to the linac axis. Deliveries used gantry positions rotated to the planned delivery angles, as well as delivery from one angle (SGAC technique). These were of the same patient plan beams with just the gantry angle being altered using a Varian Clinac 2100 linac . The verification plan was recalculated using the beams of the patient plan. For each measurement, the phantom position was adjusted to change the chamber position as prescribed in the treatment planning system. The charge was recorded beam by beam and added after the measurements. The output stability was monitored by checking the chamber reading for a 10×10 cm2 field before and after taking the actual measurements. The change in output was corrected. Leakage current has been measured and accounted for in the measurements, also. Stem effect is not included in the measurement, but it is assumed to be negligible for high‐energy photon beams, as investigated by Ma and Nahum.Measurements were performed using a Farmer‐type ionization chamber inserted in a tightly fitting hole in a B.The 20 patient plans were recalculated using the EGSnrc Monte Carlo code. The plans were for a variety of treatment sites: 15 ear, nose and throat (ENT), three head and neck, one abdominal, and one anal canal. The number of beams per plan ranged from seven up to 19 and were all for 6 MV photon beams. Clinical composite nonstandard field deliveries can generally be carried out using a photon beam having a nominal 6 MV energy,C.40×40 cm2, 30×30 cm2, 5×5 cm2, 4×4 cm2, and 3×3 cm2. The initial electron energy and FWHM of the radius of the initial electron beam incident on the target was varied to find the percentage depth dose, dose profile curves, and output factors that match the hospital‐measured data, providing output factors that match within 1% of measured output factor values.Plan data for the collapsed and rotated plans were imported into MMCTP from the DICOM files created by the treatment planning system. This patient plan data provided information on the structure, beam configuration, and calculated dose. The MMCTP system adjusts the template files using the data from the DICOM plan information to create the plan‐specific input files necessary for the Monte Carlo simulations using the EGSnrc codes. Monte Carlo simulations were submitted to a remote cluster. Previous tuningSimulations were run in two steps. Firstly, BEAMnrc models the photon production and particle transport in the accelerator and creates a phase space file at 70 cm SSD. Secondly, the DOSXYZnrc code models the particle transport and dose deposition in the phantom and the surrounding air. The simulations were run in two parts as a time‐saving measure because the same phase space files could be used as an input to the DOSXYZnrc simulations for both the rotated and collapsed simulations and later for the egs_chamber rotated and collapsed dose calculations. Directional bremsstrahlung splitting (DBS) of 1000 was used in the simulations and photon splitting of 100 was used in DOSXYZnrc to increase the calculation efficiency.30×30×17 cm3 Solid Water phantom with the chamber centrally located within it, as used for the dose measurements. The material within the phantom was set to water and the surrounding material was set to air. The dose scoring voxels were set to a size of 0.3×0.3×0.3 cm3, as this is what is used clinically.The dose‐scoring phantom for the DOSXYZnrc simulations was generated from the CT data imported into the MMCTP program. The CT data showed a scan of a 10×10 cm2 field run. This conversion allows for direct comparison between the Monte Carlo calculated doses and those obtained from the treatment planning system.On completion, the dose files output from DOSXYZnrc are downloaded from the remote cluster and read into MMCTP. MMCTP converts the dose‐per‐incident particle to Gy using a calibration factor obtained from a Once this is completed for each beam in the plan, MMCTP adds together the dose in each voxel from the different beams to obtain the total dose from the plan. DVH data and maximum, minimum, and average doses are then derived for the chamber volume allowing for a comparison with measured values. This process was repeated for the plans for each of the 20 patients in this study.D.30×30×17 cm3 water phantom surrounded by air .In order to investigate the effects of not explicitly modeling the chamber, the egs_chamber code was used to recalculate the dose. The same phase space files that were previously generated with the BEAMnrc code were used as the source for these simulations. The Exradin A12 chamber  was modeled explicitly at 8.5 cm depth in a egs++ egs_chamber code needed to be utilized, as it is the most efficient method within the EGSnrc package of calculating the dose to the active air cavity volume of a realistic ion chamber model. While in DOSXYZnrc the chamber could have been modeled by assigning the required media to the relevant voxels in the phantom, it would be very difficult to model the chamber to any great accuracy given that the voxel sizes were 0.3×0.3×0.3 cm3 and the active volume of the chamber used is only 0.65 cm3.The Monte Carlo simulations were repeated; however, this time, it was required that the chamber was modeled in full detail with the air cavity, wall, and electrode materials, and dimensions modeled as accurately as possible to allow for an analysis of the effects of the air perturbation and pixelization of the chamber when the CT image is imported into the TPS and the previous Monte Carlo calculations. In order to do this, the egs++ cavity code is the use of photon cross‐section enhancement (XCSE). XCSE is the implementation of photon splitting on a region‐by‐region basis. This means that photon splitting can be implemented in the region of the chamber and the region surrounding it, and time is not wasted simulating the tracks of extra photons and electrons that will most likely not end up contributing to the calculated dose, which is particularly the case when the dose is being calculated to a volume which is substantially smaller than the irradiated volume. In order to implement XCSE, a special XCSE region was defined surrounding the chamber region, in which photon splitting was turned on. The optimum thickness of the XCSE shell surrounding the chamber has been found to be 1 cm.One of the advantages of the egs_chamber code, over the Ncse set to 128 for the chamber simulations and to 64 for the water sphere simulations, as these were found to be the optimum values for efficiency, as shown in These simulations were carried out with ESAVE set to 0.1 MeV, range rejection set to 256, and Ncse, for the cross‐sectional enhancement regions. The tests were run on the (a) water sphere and (b) chamber model, in a water phantom, and the range rejection‐based Russian roulette factor was set to 256 for the simulations for all enhancement factors, except for the simulation with an enhancement factor of 512 where it was set to 512, as the rejection must be larger or equal to the largest XCSE factor used. The efficiency was calculated using the equation:ε) is calculated using the CPU time required for the simulation in seconds (T) and the estimated percentage uncertainty in the resulting dose (σ).where the efficiency ; ND,w,Q0 is measured at the standards laboratory for a reference field of size 10×10 cm2; kQ,Q0 is the beam quality correction factor, which corrects for the differences between the reference beam quality Q0 at the standards laboratory and the beam quality Q of the conventional field fref and is the correction factor calculated in this work to account for the difference between the responses of the ionization chamber in the reference 10×10 cm2 field fref and the clinical IMRT field fclin.where F.A number of different complexity metrics were utilized to evaluate and quantify the complexity of the IMRT plans used in this study and, hence, examine the effect of plan complexity on dose measurement and calculation.F.1The homogeneity index (HI) is a measure of the dose homogeneity within the chamber volume, and is calculated as defined by Wu et al.D2% is the dose to 2% of the chamber volume as displayed on the cumulative DVH, D98% is the dose to 98% of the chamber volume as displayed on the cumulative DVH, and Daverage is the average dose to the chamber volume. D2% and D98% are the near maximum and near minimum doses, respectively, of the chamber volume. These concepts are defined in ICRU Report 83.where F.2There are many differing definitions of the conformity index of a dose distribution as discussed by Feuvret et al.PTVsmall is the volume of the chamber contour at the center of the phantom, RI is the reference isodose which has been defined here as the average dose to the PTVsmall volume, PTVsmall,RI is the volume of the small target which is covered by the reference isodose, and PTVCOIN,RI is the volume of the added structure of 1.5 cm border surrounding the chamber volume, which is covered by the reference isodose. When using the conformity index to evaluate clinical treatment plans, a larger value for the conformity index would be considered better . However, here a larger value (closer to 1) is considered worse, as it means that there is greater dependence on accurate chamber positioning (as there would be a large dose gradient surrounding the chamber). A low value would imply that a larger portion of the region surrounding the chamber is also covered by the reference isodose and, hence, chamber positioning is less crucial.where F.3J number of beams, is calculated by:The modulation complexity score (MCS),whereI segments per beam. The weight of each beam is taken into account by weighting each of the scores based on the number of monitor units delivered by each beam.for The aperture area variability (AAV), with A number of leaves in the leaf bank, can be calculated for each segment of the beam as follows:The leaf sequence variability (LSV), with N number of open leaves in the leaf bank, can be calculated for each segment of the beam as follows:whereThis is calculated using the coordinates of the leaf positions (pos).III.A.>0.6 . The number of beams intersecting the chamber was determined upon visual inspection of the dose distribution on the treatment planning system and a beam was considered to intersect the chamber if its dose distribution intersected the chamber contour. When this occurs, the DOSXYZnrc‐calculated doses for the collapsed plans agree with the corrected measured values to within 1.73%, and the DOSXYZnrc‐calculated doses for the rotated plans agree with corrected measurements to within 2.5% . When the complexity of the plans was investigated, only patient 6 had a low COIN value (<0.05) and patient 4 had a larger HI value (>0.15 for collapsed plans). Patient 10 had a larger HI value (0.16) also, but not as high as patient 4 (0.21). All the first ten patients agree within 3% for the rotated values . These are patients 11, 12, 13, 18, and 20. Of these, patients 12 and 20 had larger HI values and patients 13 and 18 had low COIN values. Though it is not clear why patient 11 failed, it could just be an inaccurate measurement, as this analysis was carried out retrospectively.From the second ten patients (the ones who failed collapsed QA), five have Based on the Monte Carlo and measured value differences, patients 15 and 17 would also have failed and also have larger HI values. For the rotated measurements, the TPS values all agree within 3%, except for patient 20 whose plan has a low COIN value in the rotated setup. Monte Carlo calculations all agree with measurements to within 3% except patient 17, whose plan has a slightly larger HI value but not the highest; it does, however, have the lowest MCS score — making it the most complex plan.B.kQclin,Qfclin,fref factors lead to improved agreement (compared to the agreement between egs_chamber and uncorrected measurements) between the measured values and egs_chamber dose‐to‐water values in the majority of cases — 14 out of 20 for collapsed delivery and 11 out of 20 for rotated beam delivery  concealing inaccuracies in the measured dose values. It is, however, still necessary to perform the more detailed simulation, as it is the only way of accurately calculating the kQclin,Qfclin,fref factors used to correct the measurements for comparison with DOSXYZnrc dose values. The greater difference for the collapsed measurements could also be due to the inherent experimental uncertainties that could contribute differently in the rotated versus the collapsed method. For example, a small source‐to‐surface distance error would partially cancel out in the rotated delivery method, whereas in the collapsed method, it would have a systematic contribution to the error.From C.kQclin,Qfclin,fref values. In kQclin,Qfclin,fref values deviate from the ideal value of 1. If HI is less than 0.05, then the kQclin,Qfclin,fref values do not deviate from unity by more than 1%. This is consistent with Chung et al.kQclin,Qfclin,fref values for the rotated plans start to deviate more from 1 at lower HI values than for collapsed plans, but, overall, the HI values for the rotated plans are lower than those for the collapsed plans and the kQclin,Qfclin,fref values for the rotated plans as a whole deviate less from 1.From kQclin,Qfclin,fref values deviate more from 1 for larger COIN values. This is again in agreement with the results obtained by Chung and colleagues. This effect is more pronounced in the collapsed plans, which is most likely due to the more heterogeneous dose distributions in this type of beam setup.In kQclin,Qfclin,fref values deviate more from 1 for more complex plans . This is to be expected as more complex plans are classed as ones where the beamlets are more complex in shape . This is also much more pronounced for the collapsed beams, but this is again presumably due to the higher dose inhomogeneity within the chamber in this delivery format.In kQclin,Qfclin,fref values deviate more from 1 when the fraction of beams that directly intersect the chamber volume is lower. This is in agreement with what has been previously reported by Capote et al.In kQclin,Qfclin,fref values show a much greater spread in the values, particularly at the higher values for the dose homogeneity index  should be chosen to place the chamber when doing QA measurements.From these results it can be seen that more complex plans  are more likely to have poor agreement between measured data and the TPS data and are more likely to need an added correction factor V.kQclin,Qfclin,fref values deviate less from 1 than collapsed mode. However, if a collapsed technique must be used, it would be worthwhile to take into account the homogeneity of the dose in the region in which the measurement is to be performed, the complexity of the plan, and the resulting dose distribution.This work shows the potential of using Monte Carlo techniques to assess and validate dosimetric quality assurance techniques, which is of particular importance when assessing current QA techniques to decide on a method of best practice or when implementing new treatment modalities. Based on this investigation, it is apparent that rotated beam delivery for absolute dose measurement is more consistent, as the We acknowledge the help of Brian Hooten at Standard Imaging for providing blueprints for the ionization chamber model.