Richard D Neal

Bio: Richard D Neal is an academic researcher from University of Leeds. The author has contributed to research in topics: Cancer & Randomized controlled trial. The author has an hindex of 45, co-authored 196 publications receiving 8028 citations. Previous affiliations of Richard D Neal include Bangor University & University of Wales.

Is increased time to diagnosis and treatment in symptomatic cancer associated with poorer outcomes? Systematic review

Abstract: It is unclear whether more timely cancer diagnosis brings favourable outcomes, with much of the previous evidence, in some cancers, being equivocal. We set out to determine whether there is an association between time to diagnosis, treatment and clinical outcomes, across all cancers for symptomatic presentations. Systematic review of the literature and narrative synthesis. We included 177 articles reporting 209 studies. These studies varied in study design, the time intervals assessed and the outcomes reported. Study quality was variable, with a small number of higher-quality studies. Heterogeneity precluded definitive findings. The cancers with more reports of an association between shorter times to diagnosis and more favourable outcomes were breast, colorectal, head and neck, testicular and melanoma. This is the first review encompassing many cancer types, and we have demonstrated those cancers in which more evidence of an association between shorter times to diagnosis and more favourable outcomes exists, and where it is lacking. We believe that it is reasonable to assume that efforts to expedite the diagnosis of symptomatic cancer are likely to have benefits for patients in terms of improved survival, earlier-stage diagnosis and improved quality of life, although these benefits vary between cancers.

The Aarhus statement : improving design and reporting of studies on early cancer diagnosis.

Abstract: Early diagnosis is a key factor in improving the outcomes of cancer patients. A greater understanding of the pre-diagnostic patient pathways is vital yet, at present, research in this field lacks consistent definitions and methods. As a consequence much early diagnosis research is difficult to interpret. A consensus group was formed with the aim of producing guidance and a checklist for early cancer-diagnosis researchers. A consensus conference approach combined with nominal group techniques was used. The work was supported by a systematic review of early diagnosis literature, focussing on existing instruments used to measure time points and intervals in early cancer-diagnosis research. A series of recommendations for definitions and methodological approaches is presented. This is complemented by a checklist that early diagnosis researchers can use when designing and conducting studies in this field. The Aarhus checklist is a resource for early cancer-diagnosis research that should promote greater precision and transparency in both definitions and methods. Further work will examine whether the checklist can be readily adopted by researchers, and feedback on the guidance will be used in future updates.

The expanding role of primary care in cancer control

Abstract: The nature of cancer control is changing, with an increasing emphasis, fuelled by public and political demand, on prevention, early diagnosis, and patient experience during and after treatment. At the same time, primary care is increasingly promoted, by governments and health funders worldwide, as the preferred setting for most health care for reasons of increasing need, to stabilise health-care costs, and to accommodate patient preference for care close to home. It is timely, then, to consider how this expanding role for primary care can work for cancer control, which has long been dominated by highly technical interventions centred on treatment, and in which the contribution of primary care has been largely perceived as marginal. In this Commission, expert opinion from primary care and public health professionals with academic and clinical cancer expertise—from epidemiologists, psychologists, policy makers, and cancer specialists—has contributed to a detailed consideration of the evidence for cancer control provided in primary care and community care settings. Ranging from primary prevention to end-of-life care, the scope for new models of care is explored, and the actions needed to effect change are outlined. The strengths of primary care—its continuous, coordinated, and comprehensive care for individuals and families—are particularly evident in prevention and diagnosis, in shared follow-up and survivorship care, and in end-of-life care. A strong theme of integration of care runs throughout, and its elements (clinical, vertical, and functional) and the tools needed for integrated working are described in detail. All of this change, as it evolves, will need to be underpinned by new research and by continuing and shared multiprofessional development.

Variation in number of general practitioner consultations before hospital referral for cancer: findings from the 2010 National Cancer Patient Experience Survey in England

Abstract: Summary Background Information from patient surveys can help to identify patient groups and cancers with the greatest potential for improvement in the experience and timeliness of cancer diagnosis. We aimed to examine variation in the number of pre-referral consultations with a general practitioner between patients with different cancers and sociodemographic characteristics. Methods We analysed data from 41 299 patients with 24 different cancers who took part in the 2010 National Cancer Patient Experience Survey in England. We examined variation in the number of general practitioner consultations with cancer symptoms before hospital referral to diagnose cancer. Logistic regression was used to identify independent predictors of three or more pre-referral consultations, adjusting for cancer type, age, sex, deprivation quintile, and ethnic group. Findings We identified wide variation between cancer types in the proportion of patients who had visited their general practitioner three or more times before hospital referral (7·4% [625 of 8408] for breast cancer and 10·1% [113 of 1124] for melanoma; 41·3% [193 of 467] for pancreatic cancer and 50·6% [939 of 1854] for multiple myeloma). In multivariable analysis, with patients with rectal cancer as the reference group, those with subsequent diagnosis of multiple myeloma (odds ratio [OR] 3·42, 95% CI 3·01–3·90), pancreatic cancer (2·35, 1·91–2·88), stomach cancer (1·96, 1·65–2·34), and lung cancer (1·68, 1·48–1·90) were more likely to have had three or more pre-referral consultations; conversely patients with subsequent diagnosis of breast cancer (0·19; 0·17–0·22), melanoma (0·34, 0·27–0·43), testicular cancer (0·47, 0·33–0·67), and endometrial cancer (0·59, 0·49–0·71) were more likely to have been referred to hospital after only one or two consultations. The probability of three or more pre-referral consultations was greater in young patients (OR for patients aged 16–24 years vs 65–74 years 2·12, 95% CI 1·63–2·75; p vs white 1·73, 1·45–2·08; p vs white 1·83, 1·51–2·23; p vs men 1·28, 1·21–1·36; p vs men 2·31, 95% CI 1·98–2·69) and no apparent ethnic group differences in young patients aged 16–24 years, whilst the only cancers without an apparent age gradient were testicular cancer and mesothelioma. Interpretation Our findings could help to prioritise and stratify early diagnosis initiatives and research, focusing on patients with cancers and sociodemographic characteristics with the largest potential for improvement. Funding None.

Delays in the diagnosis of six cancers: analysis of data from the National Survey of NHS Patients : Cancer

Abstract: The aim of this paper is to describe and compare components of diagnostic delay (patient, primary care, referral, secondary care) for six cancers (breast, colorectal, lung, ovarian, prostate and non-Hodgkin's lymphoma), and to compare delays in patients who saw their GP prior to diagnosis with those who did not. Secondary data analysis of The National Survey of NHS Patients: Cancer was undertaken (65 192 patients). Breast cancer patients experienced the shortest total delays (mean 55.2 days), followed by lung (88.5), ovarian (90.3), non-Hodgkin's lymphoma (102.8), colorectal (125.7) and prostate (148.5). Trends were similar for all components of delay. Compared with patient and primary care delays, referral delays and secondary care delays were much shorter. Patients who saw their GP prior to diagnosis experienced considerably longer total diagnostic delays than those who did not. There were significant differences in all components of delay between the six cancers. Reducing diagnostic delays with the intention of increasing the proportion of early stage cancers may improve cancer survival in the UK, which is poorer than most other European countries. Interventions aimed at reducing patient and primary care delays need to be developed and their effect on diagnostic stage and psychological distress evaluated.

Effectiveness and efficiency of guideline dissemination and implementation strategies

Abstract: OBJECTIVES: To undertake a systematic review of the effectiveness and costs of different guideline development, dissemination and implementation strategies. To estimate the resource implications of these strategies. To develop a framework for deciding when it is efficient to develop and introduce clinical guidelines. DATA SOURCES: MEDLINE, Healthstar, Cochrane Controlled Trial Register, EMBASE, SIGLE and the specialised register of the Cochrane Effective Practice and Organisation of Care (EPOC) group. REVIEW METHODS: Single estimates of dichotomous process variables were derived for each study comparison based upon the primary end-point or the median measure across several reported end-points. Separate analyses were undertaken for comparisons of different types of intervention. The study also explored whether the effects of multifaceted interventions increased with the number of intervention components. Studies reporting economic data were also critically appraised. A survey to estimate the feasibility and likely resource requirements of guideline dissemination and implementation strategies in UK settings was carried out with key informants from primary and secondary care. RESULTS: In total, 235 studies reporting 309 comparisons met the inclusion criteria; of these 73% of comparisons evaluated multifaceted interventions, although the maximum number of replications of a specific multifaceted intervention was 11 comparisons. Overall, the majority of comparisons reporting dichotomous process data observed improvements in care; however, there was considerable variation in the observed effects both within and across interventions. Commonly evaluated single interventions were reminders, dissemination of educational materials, and audit and feedback. There were 23 comparisons of multifaceted interventions involving educational outreach. The majority of interventions observed modest to moderate improvements in care. No relationship was found between the number of component interventions and the effects of multifaceted interventions. Only 29.4% of comparisons reported any economic data. The majority of studies only reported costs of treatment; only 25 studies reported data on the costs of guideline development or guideline dissemination and implementation. The majority of studies used process measures for their primary end-point, despite the fact that only three guidelines were explicitly evidence based (and may not have been efficient). Respondents to the key informant survey rarely identified existing budgets to support guideline dissemination and implementation strategies. In general, the respondents thought that only dissemination of educational materials and short (lunchtime) educational meetings were generally feasible within current resources. CONCLUSIONS: There is an imperfect evidence base to support decisions about which guideline dissemination and implementation strategies are likely to be efficient under different circumstances. Decision makers need to use considerable judgement about how best to use the limited resources they have for clinical governance and related activities to maximise population benefits. They need to consider the potential clinical areas for clinical effectiveness activities, the likely benefits and costs required to introduce guidelines and the likely benefits and costs as a result of any changes in provider behaviour. Further research is required to: develop and validate a coherent theoretical framework of health professional and organisational behaviour and behaviour change to inform better the choice of interventions in research and service settings, and to estimate the efficiency of dissemination and implementation strategies in the presence of different barriers and effect modifiers.

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

Abstract: The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.

Data Resource Profile: Clinical Practice Research Datalink (CPRD)

Abstract: The Clinical Practice Research Datalink (CPRD) is an ongoing primary care database of anonymised medical records from general practitioners, with coverage of over 11.3 million patients from 674 practices in the UK. With 4.4 million active (alive, currently registered) patients meeting quality criteria, approximately 6.9% of the UK population are included and patients are broadly representative of the UK general population in terms of age, sex and ethnicity. General practitioners are the gatekeepers of primary care and specialist referrals in the UK. The CPRD primary care database is therefore a rich source of health data for research, including data on demographics, symptoms, tests, diagnoses, therapies, health-related behaviours and referrals to secondary care. For over half of patients, linkage with datasets from secondary care, disease-specific cohorts and mortality records enhance the range of data available for research. The CPRD is very widely used internationally for epidemiological research and has been used to produce over 1000 research studies, published in peer-reviewed journals across a broad range of health outcomes. However, researchers must be aware of the complexity of routinely collected electronic health records, including ways to manage variable completeness, misclassification and development of disease definitions for research.

Poverty impedes cognitive function

Abstract: The poor often behave in less capable ways, which can further perpetuate poverty. We hypothesize that poverty directly impedes cognitive function and present two studies that test this hypothesis. First, we experimentally induced thoughts about finances and found that this reduces cognitive performance among poor but not in well-off participants. Second, we examined the cognitive function of farmers over the planting cycle. We found that the same farmer shows diminished cognitive performance before harvest, when poor, as compared with after harvest, when rich. This cannot be explained by differences in time available, nutrition, or work effort. Nor can it be explained with stress: Although farmers do show more stress before harvest, that does not account for diminished cognitive performance. Instead, it appears that poverty itself reduces cognitive capacity. We suggest that this is because poverty-related concerns consume mental resources, leaving less for other tasks. These data provide a previously unexamined perspective and help explain a spectrum of behaviors among the poor. We discuss some implications for poverty policy.