Proactive breast cancer risk assessment in primary care: a review based on the principles of screening.

In the UK, the National Institute for Health and Care Excellence (NICE) recommends that women at moderate or high risk of breast cancer be offered risk-reducing medication and enhanced breast screening/surveillance. In June 2022, NICE withdrew a statement recommending assessment of risk in primary care only when women present with concerns. This shift to the proactive assessment of risk substantially changes the role of primary care, in effect paving the way for a primary care-based screening programme to identify those at moderate or high risk of breast cancer. In this article, we review the literature surrounding proactive breast cancer risk assessment within primary care against the consolidated framework for screening. We find that risk assessment for women under 50 years currently satisfies many of the standard principles for screening. Most notably, there are large numbers of women at moderate or high risk currently unidentified, risk models exist that can identify those women with reasonable accuracy, and management options offer the opportunity to reduce breast cancer incidence and mortality in that group. However, there remain a number of uncertainties and research gaps, particularly around the programme/system requirements, that need to be addressed before these benefits can be realised.

Towards implementation of comprehensive breast cancer risk prediction tools in health care for personalised prevention.

Advances in knowledge about breast cancer risk factors have led to the development of more comprehensive risk models. These integrate information on a variety of risk factors such as lifestyle, genetics, family history, and breast density. These risk models have the potential to deliver more personalised breast cancer prevention. This is through improving accuracy of risk estimates, enabling more effective targeting of preventive options and creating novel prevention pathways through enabling risk estimation in a wider variety of populations than currently possible. The systematic use of risk tools as part of population screening programmes is one such example. A clear understanding of how such tools can contribute to the goal of personalised prevention can aid in understanding and addressing barriers to implementation. In this paper we describe how emerging models, and their associated tools can contribute to the goal of personalised healthcare for breast cancer through health promotion, early disease detection (screening) and improved management of women at higher risk of disease. We outline how addressing specific challenges on the level of communication, evidence, evaluation, regulation, and acceptance, can facilitate implementation and uptake.

A community jury study exploring the public acceptability of using risk stratification to determine eligibility for cancer screening.

INTRODUCTION: Using risk stratification to determine eligibility for cancer screening is likely to improve the efficiency of screening programmes by targeting resources towards those most likely to benefit. We aimed to explore the implications of this approach from a societal perspective by understanding public views on the most acceptable stratification strategies. METHODS: We conducted three online community juries with 9 or 10 participants in each. Participants were purposefully sampled by age (40-79 years), sex, ethnicity, social grade and English region. On the first day, participants were informed of the potential benefits and harms of cancer screening and the implications of different ways of introducing stratification using scenarios based on phenotypic and genetic risk scores. On the second day, participants deliberated to reach a verdict on the research question, 'Which approach(es) to inviting people to screening are acceptable, and under what circumstances?' Deliberations and feedback were recorded and analysed using thematic analysis. RESULTS: Across the juries, the principle of risk stratification was generally considered to be an acceptable approach for determining eligibility for screening. Disregarding increasing capacity, the participants considered it to enable efficient resource allocation to high-risk individuals and could see how it might help to save lives. However, there were concerns regarding fair implementation, particularly how the risk assessment would be performed at scale and how people at low risk would be managed. Some favoured using the most accurate risk prediction model whereas others thought that certain risk factors should be prioritized (particularly factors considered as non-modifiable and relatively stable, such as genetics and family history). Transparently justifying the programme and public education about cancer risk emerged as important contributors to acceptability. CONCLUSION: Using risk stratification to determine eligibility for cancer screening was acceptable to informed members of the public, particularly if it included risk factors they considered fair and when communicated transparently. PATIENT OR PUBLIC CONTRIBUTION: Two patient and public involvement representatives were involved throughout this study. They were not involved in synthesizing the results but contributed to producing study materials, co-facilitated the community juries and commented on the interpretation of the findings and final report.

Rapid systematic review to identify key barriers to access, linkage, and use of local authority administrative data for population health research, practice, and policy in the United Kingdom.

BACKGROUND: Improving data access, sharing, and linkage across local authorities and other agencies can contribute to improvements in population health. Whilst progress is being made to achieve linkage and integration of health and social care data, issues still exist in creating such a system. As part of wider work to create the Cambridge Child Health Informatics and Linked Data (Cam-CHILD) database, we wanted to examine barriers to the access, linkage, and use of local authority data. METHODS: A systematic literature search was conducted of scientific databases and the grey literature. Any publications reporting original research related to barriers or enablers of data linkage of or with local authority data in the United Kingdom were included. Barriers relating to the following issues were extracted from each paper: funding, fragmentation, legal and ethical frameworks, cultural issues, geographical boundaries, technical capability, capacity, data quality, security, and patient and public trust. RESULTS: Twenty eight articles were identified for inclusion in this review. Issues relating to technical capacity and data quality were cited most often. This was followed by those relating to legal and ethical frameworks. Issue relating to public and patient trust were cited the least, however, there is considerable overlap between this topic and issues relating to legal and ethical frameworks. CONCLUSIONS: This rapid review is the first step to an in-depth exploration of the barriers to data access, linkage and use; a better understanding of which can aid in creating and implementing effective solutions. These barriers are not novel although they pose specific challenges in the context of local authority data.

Understanding polygenic models, their development and the potential application of polygenic scores in healthcare.

The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research in this area has proliferated with several proposed methods of generating polygenic scores. This has been driven by the availability of larger data sets, primarily from genome-wide association studies and concomitant developments in statistical methodologies. Here we provide an overview of the methodological aspects of polygenic model construction. In addition, we consider the state of the field and implications for potential applications of polygenic scores for risk estimation within healthcare.

Systematic Review and Meta-Analysis of the Birth Prevalence of Orofacial Clefts in Low- and Middle-Income Countries.

BACKGROUND: In the last comprehensive review of the literature published in 2002, little information on the prevalence of orofacial clefts was available from low- and middle-income countries (LMICs). OBJECTIVE: To analyze published data on the birth prevalence of cleft lip and/or palate (CL/P) from LMIC. DESIGN: Systematic review of the literature and meta-analysis of data from original papers on the birth prevalence of cleft lip and/or cleft palate (CL/P) in LMICs between 1990 and 2014. Secondary inclusion criteria were developed to analyze lower-quality studies from countries with scarce data. MAIN OUTCOME MEASURE: Birth prevalence of undifferentiated CL/P (with or without associated syndrome or other anomaly). RESULTS: Twenty-eight studies met strict inclusion criteria. Among 31,475,278 total births, the pooled birth prevalence of undifferentiated CL/P was 1.38 per 1000 births (95% confidence interval [CI]: 1.20 to 1.56). Four studies met criteria for secondary analysis, providing data on 75,627 births, with a pooled prevalence of 0.75 CL/P cases per 1000 births (95% CI: 0.56 to 0.95). Comparison of studies was limited by variable definitions of cases and of the reference population and by inconsistent reporting of outcomes. There is significant heterogeneity in the findings. CONCLUSIONS: In LMICs, approximately 1 in every 730 children is born with CL/P. To optimize comparability across settings, future research should use a standard classification system and standard criteria for data collection and presentation. As clefting is associated with deprivation, understanding the true scale, risks, and preventive measures for orofacial clefts in LMIC is a matter of both scientific and humanitarian importance.

Systematic review and meta-analysis to estimate the birth prevalence of five inherited metabolic diseases.

Many newborn screening programmes now use tandem mass spectrometry in order to screen for a variety of diseases. However, countries have embraced this technology with a differing pace of change and for different conditions. This has been facilitated by the ability of this diagnostic method to limit analysis to specific metabolites of interest, enabling targeted screening for particular conditions. MS/MS was introduced in 2009 in England to implement newborn bloodspot screening for medium chain acyl-CoA dehydrogenase deficiency (MCADD) raising the possibility of screening for other inherited metabolic disorders. Recently, a pilot screening programme was conducted in order to evaluate the health and economic consequences of screening for five additional inherited metabolic disorders in England. As part of this study we conducted a systematic review and meta-analysis to estimate the birth prevalence of these conditions: maple syrup urine disease, homocystinuria (pyridoxine unresponsive), glutaric aciduria type I, isovaleric acidaemia and long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency including trifunctional protein deficiency. We identified a total of 99 studies that were able to provide information on the prevalence of one or more of the disorders. The vast majority of studies were of screening programmes with some reporting on clinically detected cases.

Informatics and clinical genome sequencing: opening the black box.

Adoption of whole-genome sequencing as a routine biomedical tool is dependent not only on the availability of new high-throughput sequencing technologies, but also on the concomitant development of methods and tools for data collection, analysis, and interpretation. It would also be enormously facilitated by the development of decision support systems for clinicians and consideration of how such information can best be incorporated into care pathways. Here we present an overview of the data analysis and interpretation pipeline, the wider informatics needs, and some of the relevant ethical and legal issues.

Systematic review of birth prevalence of neural tube defects in India.

BACKGROUND: Neural tube defects are one of the most prevalent congenital anomalies. Data on the total birth prevalence, live birth and stillbirth prevalence of neural tube defects in India are lacking. The objective of this study is to conduct a systematic review of birth prevalence of neural tube defects in India and compare it with existing estimates. METHODS: A PubMed search identified 463 articles, of which 19 articles were eligible for inclusion in the review. Meta-analysis was used to estimate the overall birth prevalence of neural tube defects and to investigate the variation among studies identified by this review. RESULTS: The 19 articles reported a total of 308,387 births, among which 1310 cases of neural tube defects were reported, giving an overall birth prevalence of 4.1 per 1000 (95% confidence interval [CI], 3.1-5.4). The live birth and stillbirth prevalence of neural tube defects was 1.3 per 1000 births (95% CI, 0.9-1.8) and 1.7 per 1000 births (95% CI, 0.7-4.0), respectively. Among the neural tube defects, the reported prevalence of anencephaly was highest at 2.1 per 1000 births (95% CI, 1.6-2.8) followed by spina bifida at 1.9 per 1000 births (95% CI, 1.4-2.7). CONCLUSION: The systematic review suggests that neural tube defects contribute to a significant number of live births and stillbirths in India, suggesting that preconception folic acid supplementation should be an essential element of reproductive health services.

Public Preferences for Determining Eligibility for Screening in Risk-Stratified Cancer Screening Programs: A Discrete Choice Experiment.

BACKGROUND: Risk stratification has been proposed to improve the efficiency of population-level cancer screening. We aimed to describe and quantify the relative importance of different attributes of potential screening programs among the public, focusing on stratifying eligibility. METHODS: We conducted a discrete choice experiment in which respondents selected between 2 hypothetical screening programs in a series of 9 questions. We presented the risk factors used to determine eligibility (age, sex, or lifestyle or genetic risk scores) and anticipated outcomes based on eligibility criteria with different sensitivity and specificity levels. We performed conditional logit regression models and used the results to estimate preferences for different approaches. We also analyzed free-text comments on respondents' views on the programs. RESULTS: A total of 1,172 respondents completed the survey. Sensitivity was the most important attribute (7 and 11 times more important than specificity and risk factors, respectively). Eligibility criteria based on age and sex or genetics were preferred over age alone and lifestyle risk scores. Phenotypic and polygenic risk prediction models would be more acceptable than screening everyone aged 55 to 70 y if they had high discrimination (area under the receiver-operating characteristic curve ≥0.75 and 0.80, respectively). LIMITATIONS: Although our sample was representative with respect to age, sex, and ethnicity, it may not be representative of the UK population regarding other important characteristics. Also, some respondents may have not understood all the information provided to inform decision making. CONCLUSIONS: The public prioritized lives saved from cancer over reductions in numbers screened or experiencing unnecessary follow-up. Incorporating personal-level risk factors into screening eligibility criteria is acceptable to the public if it increases sensitivity; therefore, maximizing sensitivity in model development and communication could increase uptake. HIGHLIGHTS: The public prioritized lives saved when considering changing from age-based eligibility criteria to risk-stratified cancer screening over reductions in numbers of people being screened or experiencing unnecessary follow-up.The risk stratification strategy used to do this was the least important component, although age plus sex or genetics were relatively preferable to using age alone and lifestyle risk scores.Communication strategies that emphasize improvements in the numbers of cancers detected or not missed across the population are more likely to be salient than reductions in unnecessary investigations or follow-up among some groups.Future research should focus on developing implementation strategies that maximize gains in sensitivity within the context of resource constraints and how to present attributes relating to specificity to facilitate understanding and informed decision making.

How can we address the uncertainties regarding the potential clinical utility of polygenic score-based tests?

As common low penetrance variants associated with diseases are uncovered, attempts continue to be made to harness this knowledge for improving healthcare. Polygenic scores have been developed as the mechanism by which knowledge of common variants can be used to investigate genetic contributions to disease risk. They serve as a biomarker to provide an estimate of the genetic liability for a particular disease. Discussion continues as to whether polygenic scores are a useful biomarker and their readiness for incorporation into clinical and public health practice. In this paper, we investigate the key challenges that need to be addressed, in the description and assessment of the clinical utility of polygenic score-based tests for use in clinical and public health practice.

The risk of developing many common diseases, such as heart disease is influenced by both genetic and lifestyle factors. Polygenic scores (PGS) are one way of assessing an individual's risk of developing certain diseases. There is still uncertainty as to whether and how to use PGS for individual care. Much of this is because it is unclear as to whether tests that give a PGS can provide useful information for the care of individuals and patients as part of prevention or healthcare pathways. In this paper, we describe some of the challenges that need to be addressed, so that we can move forward and better understand when and how to use these tests for population and individual benefit.

A Scoping Review of Approaches to Improving Quality of Data Relating to Health Inequalities.

Identifying and monitoring of health inequalities requires good-quality data. The aim of this work is to systematically review the evidence base on approaches taken within the healthcare context to improve the quality of data for the identification and monitoring of health inequalities and describe the evidence base on the effectiveness of such approaches or recommendations. Peer-reviewed scientific journal publications, as well as grey literature, were included in this review if they described approaches and/or made recommendations to improve data quality relating to the identification and monitoring of health inequalities. A thematic analysis was undertaken of included papers to identify themes, and a narrative synthesis approach was used to summarise findings. Fifty-seven papers were included describing a variety of approaches. These approaches were grouped under four themes: policy and legislation, wider actions that enable implementation of policies, data collection instruments and systems, and methodological approaches. Our findings indicate that a variety of mechanisms can be used to improve the quality of data on health inequalities at different stages (prior to, during, and after data collection). These findings can inform us of actions that can be taken by those working in local health and care services on approaches to improving the quality of data on health inequalities.

Risk-Adjusted Cancer Screening and Prevention (RiskAP): Complementing Screening for Early Disease Detection by a Learning Screening Based on Risk Factors.

Background: Risk-adjusted cancer screening and prevention is a promising and continuously emerging option for improving cancer prevention. It is driven by increasing knowledge of risk factors and the ability to determine them for individual risk prediction. However, there is a knowledge gap between evidence of increased risk and evidence of the effectiveness and efficiency of clinical preventive interventions based on increased risk. This gap is, in particular, aggravated by the extensive availability of genetic risk factor diagnostics, since the question of appropriate preventive measures immediately arises when an increased risk is identified. However, collecting proof of effective preventive measures, ideally by prospective randomized preventive studies, typically requires very long periods of time, while the knowledge about an increased risk immediately creates a high demand for action. Summary: Therefore, we propose a risk-adjusted prevention concept that is based on the best current evidence making needed and appropriate preventive measures available, and which is constantly evaluated through outcome evaluation, and continuously improved based on these results. We further discuss the structural and procedural requirements as well as legal and socioeconomical aspects relevant for the implementation of this concept.

Publisher Correction: Personalized early detection and prevention of breast cancer: ENVISION consensus statement.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Personalized early detection and prevention of breast cancer: ENVISION consensus statement.

The European Collaborative on Personalized Early Detection and Prevention of Breast Cancer (ENVISION) brings together several international research consortia working on different aspects of the personalized early detection and prevention of breast cancer. In a consensus conference held in 2019, the members of this network identified research areas requiring development to enable evidence-based personalized interventions that might improve the benefits and reduce the harms of existing breast cancer screening and prevention programmes. The priority areas identified were: 1) breast cancer subtype-specific risk assessment tools applicable to women of all ancestries; 2) intermediate surrogate markers of response to preventive measures; 3) novel non-surgical preventive measures to reduce the incidence of breast cancer of poor prognosis; and 4) hybrid effectiveness-implementation research combined with modelling studies to evaluate the long-term population outcomes of risk-based early detection strategies. The implementation of such programmes would require health-care systems to be open to learning and adapting, the engagement of a diverse range of stakeholders and tailoring to societal norms and values, while also addressing the ethical and legal issues. In this Consensus Statement, we discuss the current state of breast cancer risk prediction, risk-stratified prevention and early detection strategies, and their implementation. Throughout, we highlight priorities for advancing each of these areas.

Methods to estimate access to care and the effect of interventions on the outcomes of congenital disorders.

In the absence of intervention, early-onset congenital disorders lead to pregnancy loss, early death, or disability. Currently, lack of epidemiological data from many settings limits the understanding of the burden of these conditions, thus impeding health planning, policy-making, and commensurate resource allocation. The Modell Global Database of Congenital Disorders (MGDb) seeks to meet this need by combining general biological principles with observational and demographic data, to generate estimates of the burden of congenital disorders. A range of interventions along the life course can modify adverse outcomes associated with congenital disorders. Hence, access to and quality of services available for the prevention and care of congenital disorders affects both their birth prevalence and the outcomes for affected individuals. Information on this is therefore important to enable burden estimates for settings with limited observational data, but is lacking from many settings. This paper, the third in this special issue on methods used in the MGDb for estimating the global burden of congenital disorders, describes key interventions that impact on outcomes of congenital disorders and methods used to estimate their coverage where empirical data are not available.

Estimates of global and regional prevalence of neural tube defects for 2015: a systematic analysis.

Neural tube defects (NTDs) are associated with substantial mortality, morbidity, disability, and psychological and economic costs. Many are preventable with folic acid, and access to appropriate services for those affected can improve survival and quality of life. We used a compartmental model to estimate global and regional birth prevalence of NTDs (live births, stillbirths, and elective terminations of pregnancy) and subsequent under-5 mortality. Data were identified through web-based reviews of birth defect registry databases and systematic literature reviews. Meta-analyses were undertaken where appropriate. For 2015, our model estimated 260,100 (uncertainty interval (UI): 213,800-322,000) NTD-affected birth outcomes worldwide (prevalence 18.6 (15.3-23.0)/10,000 live births). Approximately 50% of cases were elective terminations of pregnancy for fetal anomalies (UI: 59,300 (47,900-74,500)) or stillbirths (57,800 (UI: 35,000-88,600)). Of NTD-affected live births, 117,900 (∼75%) (UI: 105,500-186,600) resulted in under-5 deaths. Our systematic review showed a paucity of high-quality data in the regions of the world with the highest burden. Despite knowledge about prevention, NTDs remain highly prevalent worldwide. Lack of surveillance and incomplete ascertainment of affected pregnancies make NTDs invisible to policy makers. Improved surveillance of all adverse outcomes is needed to improve the robustness of total NTD prevalence estimation, evaluate effectiveness of prevention through folic acid fortification, and improve outcomes through care and rehabilitation.

Chromosomal disorders: estimating baseline birth prevalence and pregnancy outcomes worldwide.

Chromosomal disorders, of which Down syndrome is the most common, can cause multi-domain disability. In addition, compared to the general population, there is a higher frequency of death before the age of five. In many settings, large gaps in data availability have hampered policy-making, programme priorities and resource allocation for these important conditions. We have developed methods, which overcome this lack of data and allow estimation of the burden of affected pregnancies and their outcomes in different settings worldwide. For example, the methods include a simple equation relating the percentage of mothers 35 and over to Down syndrome birth prevalence. The results obtained provide a starting point for consideration of services that can be implemented for the care and prevention of these disorders.

Rare single gene disorders: estimating baseline prevalence and outcomes worldwide.

As child mortality rates overall are decreasing, non-communicable conditions, such as genetic disorders, constitute an increasing proportion of child mortality, morbidity and disability. To date, policy and public health programmes have focused on common genetic disorders. Rare single gene disorders are an important source of morbidity and premature mortality for affected families. When considered collectively, they account for an important public health burden, which is frequently under-recognised. To document the collective frequency and health burden of rare single gene disorders, it is necessary to aggregate them into large manageable groupings and take account of their family implications, effective interventions and service needs. Here, we present an approach to estimate the burden of these conditions up to 5 years of age in settings without empirical data. This approaches uses population-level demographic data, combined with assumptions based on empirical data from settings with data available, to provide population-level estimates which programmes and policy-makers when planning services can use.