Nadeem Qureshi

Family History in Public Health Practice: A Genomic Tool for Disease Prevention and Health Promotion*

Family history is a risk factor for many chronic diseases, including cancer, cardiovascular disease, and diabetes. Professional guidelines usually include family history to assess health risk, initiate interventions, and motivate behavioral changes. The advantages of family history over other genomic tools include a lower cost, greater acceptability, and a reflection of shared genetic and environmental factors. However, the utility of family history in public health has been poorly explored. To establish family history as a public health tool, it needs to be evaluated within the ACCE framework (analytical validity; clinical validity; clinical utility; and ethical, legal, and social issues). Currently, private and public organizations are developing tools to collect standardized family histories of many diseases. Their goal is to create family history tools that have decision support capabilities and are compatible with electronic health records. These advances will help realize the potential of family history as a public health tool.

Family history of type 2 diabetes: A population-based screening tool for prevention?

Purpose: To evaluate the use of self-reported family medical history as a potential screening tool to identify people at-risk for diabetes.Methods: The HealthStyles 2004 mail survey comprises 4345 US adults who completed a questionnaire to ascertain personal and family history of diabetes, perceived risk of diabetes, and practice of risk-reducing behaviors. Using number and type of affected relatives, respondents were ranked into three familial risk levels. Adjusted odds ratios (AORs) were obtained to evaluate associations between familial risk and prevalent diabetes, perceived risk of disease, and risk-reducing behaviors. Validity of family history as a screening tool was examined by calculating sensitivity, specificity, and positive and negative predictive values.Results: Compared to those of average risk, people with moderate and high familial risk of diabetes were more likely to report a diagnosis of diabetes (AOR: 3.6, 95% CI: 2.8, 4.7; OR: 7.6, 95% CI: 5.9, 9.8, respectively), a higher perceived risk of diabetes (AOR: 4.6, 95% CI: 3.7, 5.7; OR: 8.5, 95% CI: 6.6, 17.7, respectively), and making lifestyle changes to prevent diabetes (AOR: 2.2, 95% CI: 1.8, 2.7; OR: 4.5, 95% CI: 3.6, 5.6, respectively). A positive familial risk of diabetes identified 73% of all respondents with diabetes and correctly predicted prevalent diabetes in 21.5% of respondents.Conclusion: Family history of diabetes is not only a risk factor for the disease but is also positively associated with risk awareness and risk-reducing behaviors. It may provide a useful screening tool for detection and prevention of diabetes.

Systematic Review: Family History in Risk Assessment for Common Diseases

Wilson and associates reviewed evidence about the potential beneficial and harmful effects of routinely collecting family history information in primary care settings. They also reviewed studies th...

Effect of Adding Systematic Family History Enquiry to Cardiovascular Disease Risk Assessment in Primary Care : A Matched-Pair, Cluster Randomized Trial

BACKGROUND Evidence of the value of systematically collecting family history in primary care is limited. OBJECTIVE To evaluate the feasibility of systematically collecting family history of coronary heart disease in primary care and the effect of incorporating these data into cardiovascular risk assessment. DESIGN Pragmatic, matched-pair, cluster randomized, controlled trial. (International Standardized Randomized Controlled Trial Number Register: ISRCTN 17943542). SETTING 24 family practices in the United Kingdom. PARTICIPANTS 748 persons aged 30 to 65 years with no previously diagnosed cardiovascular risk, seen between July 2007 and March 2009. INTERVENTION Participants in control practices had the usual Framingham-based cardiovascular risk assessment with and without use of existing family history information in their medical records. Participants in intervention practices also completed a questionnaire to systematically collect their family history. All participants were informed of their risk status. Participants with high cardiovascular risk were invited for a consultation. MEASUREMENTS The primary outcome was the proportion of participants with high cardiovascular risk (10-year risk ≥ 20%). Other measures included questionnaire completion rate and anxiety score. RESULTS 98% of participants completed the family history questionnaire. The mean increase in proportion of participants classified as having high cardiovascular risk was 4.8 percentage points in the intervention practices, compared with 0.3 percentage point in control practices when family history from patient records was incorporated. The 4.5-percentage point difference between groups (95% CI, 1.7 to 7.2 percentage points) remained significant after adjustment for participant and practice characteristics (P = 0.007). Anxiety scores were similar between groups. LIMITATIONS Relatively few participants were from ethnic minority or less-educated groups. The potential to explore behavioral change and clinical outcomes was limited. Many data were missing for anxiety scores. CONCLUSION Systematically collecting family history increases the proportion of persons identified as having high cardiovascular risk for further targeted prevention and seems to have little or no effect on anxiety. PRIMARY FUNDING SOURCE Genetics Health Services Research program of the United Kingdom Department of Health.

Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews

BackgroundThis study is to identify, summarise and synthesise literature on the causes of the evidence to practice gap for complex interventions in primary care.DesignThis study is a systematic review of reviews.MethodsMEDLINE, EMBASE, CINAHL, Cochrane Library and PsychINFO were searched, from inception to December 2013. Eligible reviews addressed causes of the evidence to practice gap in primary care in developed countries. Data from included reviews were extracted and synthesised using guidelines for meta-synthesis.ResultsSeventy reviews fulfilled the inclusion criteria and encompassed a wide range of topics, e.g. guideline implementation, integration of new roles, technology implementation, public health and preventative medicine. None of the included papers used the term “cause” or stated an intention to investigate causes at all. A descriptive approach was often used, and the included papers expressed “causes” in terms of “barriers and facilitators” to implementation. We developed a four-level framework covering external context, organisation, professionals and intervention. External contextual factors included policies, incentivisation structures, dominant paradigms, stakeholders’ buy-in, infrastructure and advances in technology. Organisation-related factors included culture, available resources, integration with existing processes, relationships, skill mix and staff involvement. At the level of individual professionals, professional role, underlying philosophy of care and competencies were important. Characteristics of the intervention that impacted on implementation included evidence of benefit, ease of use and adaptability to local circumstances. We postulate that the “fit” between the intervention and the context is critical in determining the success of implementation.ConclusionsThis comprehensive review of reviews summarises current knowledge on the barriers and facilitators to implementation of diverse complex interventions in primary care. To maximise the uptake of complex interventions in primary care, health care professionals and commissioning organisations should consider the range of contextual factors, remaining aware of the dynamic nature of context. Future studies should place an emphasis on describing context and articulating the relationships between the factors identified here.Systematic review registrationPROSPERO CRD42014009410

Using family history information to promote healthy lifestyles and prevent diseases; a discussion of the evidence.

BackgroundA family history, reflecting genetic susceptibility as well as shared environmental and behavioral factors, is an important risk factor for common chronic multifactorial diseases such as cardiovascular diseases, type 2 diabetes and many cancers.DiscussionThe purpose of the present paper is to discuss the evidence for the use of family history as a tool for primary prevention of common chronic diseases, in particular for tailored interventions aimed at promoting healthy lifestyles. The following questions are addressed: (1) What is the value of family history information as a determinant of personal disease risk?; (2)How can family history information be used to motivate at-risk individuals to adopt and maintain healthy lifestyles in order to prevent disease?; and (3) What additional studies are needed to assess the potential value of family history information as a tool to promote a healthy lifestyle?SummaryIn addition to risk assessment, family history information can be used to personalize health messages, which are potentially more effective in promoting healthy lifestyles than standardized health messages. More research is needed on the evidence for the effectiveness of such a tool.

The current state of cancer family history collection tools in primary care: a systematic review

Systematic collection of family history is a prerequisite for identifying genetic risk. This study reviewed tools applicable to the primary care assessment of family history of breast, colorectal, ovarian, and prostate cancer. MEDLINE, EMBASE, CINAHL, and Cochrane Central were searched for publications. All primary study designs were included. Characteristics of the studies, the family history collection tools, and the setting were evaluated. Of 40 eligible studies, 18 relevant family history tools were identified, with 11 developed for use in primary care. Most collected information on more than one cancer and on affected relatives used self-administered questionnaires and paper-based formats. Eleven tools had been evaluated relative to current practice, demonstrating 46–78% improvement in data recording over family history recording in patient charts and 75–100% agreement with structured genetic interviews. Few tools have been developed specifically for primary care settings. The few that have been evaluated performed well. The very limited evidence, which depends in part on extrapolation from studies in settings other than primary care, suggests that systematic tools may add significant family health information compared with current primary care practice. The effect of their use on health outcomes has not been evaluated.

Controversies in familial hypercholesterolaemia: recommendations of the NICE Guideline Development Group for the identification and management of familial hypercholesterolaemia

Familial hypercholesterolaemia (FH) is one of the most common monogenic inherited conditions in clinical practice and has a prevalence of about 1 in 500, similar to type 1 diabetes. In the United Kingdom over 85% of the estimated 120 000 people who are thought to be affected remain undiagnosed. The National Institute for Health and Clinical Excellence (NICE) has recently developed a clinical guideline for the identification and management of FH.1 This review summarises some of the key controversies appraised by the guideline development group (GDG) in its consideration of the evidence, including assessing the cost-effectiveness of alternative diagnostic and identification strategies and of different low-density lipoprotein cholesterol (LDL-C)-lowering treatments. The clinical diagnosis of FH is based on personal and family history, physical examination and cholesterol concentrations. Three groups have developed clinical diagnostic tools for FH: the UK Simon Broome Register Group,2 the US MedPed Program3 and the Dutch Lipid Clinic Network.4 The Simon Broome Register criteria include cholesterol concentrations, clinical characteristics, DNA diagnosis and family history2 (box 1). The Dutch Lipid Clinic Network criteria4 use a scoring system that is effectively similar to the Simon Broome Register criteria, while the MedPed criteria use different cholesterol thresholds for different age groups and first-degree and second-degree affected relatives.3 The major difference between these systems is the use of different cut-offs for premature coronary heart disease (CHD). The Simon Broome group recommends the use of CHD <60 years in first-degree relatives and <50 years in second-degree relatives.2 The Medped criteria recommend a cut-off at age 65,3 while the Dutch suggest <55 years for men and <65 years for women.4 The GDG decided that the Simon Broome criteria should be recommended for making a clinical diagnosis because they are developed from a UK cohort …

Achieving change in primary care—effectiveness of strategies for improving implementation of complex interventions: systematic review of reviews

Objective To identify, summarise and synthesise available literature on the effectiveness of implementation strategies for optimising implementation of complex interventions in primary care. Design Systematic review of reviews. Data sources MEDLINE, EMBASE, CINAHL, Cochrane Library and PsychINFO were searched, from first publication until December 2013; the bibliographies of relevant articles were screened for additional reports. Eligibility criteria for selecting studies Eligible reviews had to (1) examine effectiveness of single or multifaceted implementation strategies, (2) measure health professional practice or process outcomes and (3) include studies from predominantly primary care in developed countries. Two reviewers independently screened titles/abstracts and full-text articles of potentially eligible reviews for inclusion. Data synthesis Extracted data were synthesised using a narrative approach. Results 91 reviews were included. The most commonly evaluated strategies were those targeted at the level of individual professionals, rather than those targeting organisations or context. These strategies (eg, audit and feedback, educational meetings, educational outreach, reminders) on their own demonstrated a small to modest improvement (2–9%) in professional practice or behaviour with considerable variability in the observed effects. The effects of multifaceted strategies targeted at professionals were mixed and not necessarily more effective than single strategies alone. There was relatively little review evidence on implementation strategies at the levels of organisation and wider context. Evidence on cost-effectiveness was limited and data on costs of different strategies were scarce and/or of low quality. Conclusions There is a substantial literature on implementation strategies aimed at changing professional practices or behaviour. It remains unclear which implementation strategies are more likely to be effective than others and under what conditions. Future research should focus on identifying and assessing the effectiveness of strategies targeted at the wider context and organisational levels and examining the costs and cost-effectiveness of implementation strategies. PROSPERO registration number CRD42014009410.

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.