5-year versus risk-category-specific screening intervals for cardiovascular disease prevention: a cohort study

Summary: Background: Clinical guidelines suggest preventive interventions such as statin therapy for individuals with a high estimated 10-year risk of major cardiovascular events. For those with a low or intermediate estimated risk, risk-factor screenings are recommended at 5-year intervals; this interval is based on expert opinion rather than on direct research evidence. Using longitudinal data on the progression of cardiovascular disease risk over time, we compared different screening intervals in terms of timely detection of high-risk individuals, cardiovascular events prevented, and health-care costs. Methods: We used data from participants in the British Whitehall II study (aged 40–64 years at baseline) who had repeated biomedical screenings at 5-year intervals and linked these data to electronic health records between baseline (Aug 7, 1991, to May 10, 1993) and June 30, 2015. We estimated participants' 10-year risk of a major cardiovascular event (myocardial infarction, cardiac death, and fatal or non-fatal stroke) using the revised Atherosclerotic Cardiovascular Disease (ASCVD) calculator. We used multistate Markov modelling to estimate optimum screening intervals on the basis of progression rates from low-risk and intermediate-risk categories to the high-risk category (ie, ≥7·5% 10-year risk of a major cardiovascular event). Our assessment criteria included person-years spent in a high-risk category before detection, the number of major cardiovascular events prevented and quality-adjusted life-years (QALYs) gained, and screening costs. Findings: Of 6964 participants (mean age 50·0 years [SD 6·0] at baseline) with 152 700 person-years of follow-up (mean follow-up 22·0 years [SD 5·0]), 1686 participants progressed to the high-risk category and 617 had a major cardiovascular event. With the 5-year screening intervals, participants spent 7866 (95% CI 7130–8658) person-years unrecognised in the high-risk group. For individuals in the low, intermediate-low, and intermediate-high risk categories, 21 alternative risk category-based screening intervals outperformed the 5-yearly screening protocol. Screening intervals at 7 years, 4 years, and 1 year for those in the low, intermediate-low, and intermediate-high-risk category would reduce the number of person-years spent unrecognised in the high-risk group by 62% (95% CI 57–66; 4894 person-years), reduce the number of major cardiovascular events by 8% (7–9; 49 events), and raise 44 QALYs (40–49) for the study population. Interpretation: In terms of timely preventive interventions, the 5-year screening intervals were unnecessarily frequent for low-risk individuals and insufficiently frequent for intermediate-risk individuals. Screening intervals based on risk-category-specific progression rates would perform better in terms of preventing major cardiovascular disease events and improving cost-effectiveness. Funding: Medical Research Council, British Heart Association, National Institutes on Aging, NordForsk, Academy of Finland

Lifestyle factors and risk of sickness absence from work: a multicohort study

Summary: Background: Lifestyle factors influence the risk of morbidity and mortality, but the extent to which they are associated with employees' absence from work due to illness is unclear. We examined the relative contributions of smoking, alcohol consumption, high body-mass index, and low physical activity to diagnosis-specific sickness absence. Methods: We did a multicohort study with individual-level data of participants of four cohorts from the UK, France, and Finland. Participants' responses to a lifestyle survey were linked to records of sickness absence episodes, typically lasting longer than 9 days; for each diagnostic category, the outcome was the total number of sickness absence days per year. We estimated the associations between lifestyle factors and sickness absence by calculating rate ratios for the number of sickness absence days per year and combining cohort-specific estimates with meta-analysis. The criteria for assessing the evidence included the strength of association, consistency across cohorts, robustness to adjustments and multiple testing, and impact assessment by use of population attributable fractions (PAF), with both internal lifestyle factor prevalence estimates and those obtained from European populations (PAFexternal). Findings: For 74 296 participants, during 446 478 person-years at risk, the most common diagnoses for sickness absence were musculoskeletal diseases (70·9 days per 10 person-years), depressive disorders (26·5 days per 10 person-years), and external causes (such as injuries and poisonings; 12·8 days per 10 person-years). Being overweight (rate ratio [adjusted for age, sex, socioeconomic status, and chronic disease at baseline] 1·30, 95% CI 1·21–1·40; PAFexternal 8·9%) and low physical activity (1·23, 1·14–1·34; 7·8%) were associated with absences due to musculoskeletal diseases; heavy episodic drinking (1·90, 1·41–2·56; 15·2%), smoking (1·70, 1·42–2·03; 11·8%), low physical activity (1·67, 1·42–1·96; 19·8%), and obesity (1·38, 1·11–1·71; 5·6%) were associated with absences due to depressive disorders; heavy episodic drinking (1·64, 1·33–2·03; 11·3%), obesity (1·48, 1·27–1·72; 6·6%), smoking (1·35, 1·20–1·53; 6·3%), and being overweight (1·20, 1·08–1·33; 6·2%) were associated with absences due to external causes; obesity (1·82, 1·40–2·36; 11·0%) and smoking (1·60, 1·30–1·98; 10·3%) were associated with absences due to circulatory diseases; low physical activity (1·37, 1·25–1·49; 12·0%) and smoking (1·27, 1·16–1·40; 4·9%) were associated with absences due to respiratory diseases; and obesity (1·67, 1·34–2·07; 9·7%) was associated with absences due to digestive diseases. Interpretation: Lifestyle factors are associated with sickness absence due to several diseases, but observational data cannot determine the nature of these associations. Future studies should investigate the cost-effectiveness of lifestyle interventions aimed at reducing sickness absence and the use of information on lifestyle for identifying groups at risk. Funding: NordForsk, British Medical Research Council, Academy of Finland, Helsinki Institute of Life Sciences, and Economic and Social Research Council

Association between change in cardiovascular risk scores and future cardiovascular disease: analyses of data from the Whitehall II longitudinal, prospective cohort study

Summary: Background: Evaluation of cardiovascular disease risk in primary care, which is recommended every 5 years in middle-aged and older adults (typical age range 40–75 years), is based on risk scores, such as the European Society of Cardiology Systematic Coronary Risk Evaluation (SCORE) and American College of Cardiology/American Heart Association Atherosclerotic Cardiovascular Disease (ASCVD) algorithms. This evaluation currently uses only the most recent risk factor assessment. We aimed to examine whether 5-year changes in SCORE and ASCVD risk scores are associated with future cardiovascular disease risk. Methods: We analysed data from the Whitehall II longitudinal, prospective cohort study for individuals with no history of stroke, myocardial infarction, coronary artery bypass graft, percutaneous coronary intervention, definite angina, heart failure, or peripheral artery disease. Participants underwent clinical examinations in 5-year intervals between Aug 7, 1991, and Dec 6, 2016, and were followed up for incident cardiovascular disease until Oct 2, 2019. Levels of, and 5-year changes in, cardiovascular disease risk were assessed using the SCORE and ASCVD risk scores and were analysed as predictors of cardiovascular disease. Harrell's C index, continuous net reclassification improvement, the Akaike information criterion, and calibration analysis were used to assess whether incorporating change in risk scores into a model including only a single risk score assessment improved the predictive performance. We assessed the levels of, and 5-year changes in, SCORE and ASCVD risk scores as predictors of cardiovascular disease and disease-free life-years using Cox proportional hazards and flexible parametric survival models. Findings: 7574 participants (5233 [69·1%] men, 2341 [30·9%] women) aged 40–75 years were included in analyses of risk score change between April 24, 1997, and Oct 2, 2019. During a mean follow-up of 18·7 years (SD 5·5), 1441 (19·0%; 1042 [72·3%] men and 399 [27·7%] women) participants developed cardiovascular disease. Adding 5-year change in risk score to a model that included only a single risk score assessment improved model performance according to Harrell's C index (from 0·685 to 0·690, change 0·004 [95% CI 0·000 to 0·008] for SCORE; from 0·699 to 0·700, change 0·001 [0·000 to 0·003] for ASCVD), the Akaike information criterion (from 17 255 to 17 200, change −57 [95% CI −97 to −13] for SCORE; from 14 739 to 14 729, change −10 [–28 to 7] for ASCVD), and the continuous net reclassification index (0·353 [95% CI 0·234 to 0·447] for SCORE; 0·232 [0·030 to 0·344] for ASCVD). Both favourable and unfavourable changes in SCORE and ASCVD were associated with cardiovascular disease risk and disease-free life-years. The associations were seen in both sexes and all age groups up to the age of 75 years. At the age of 45 years, each 2-unit improvement in risk scores was associated with an additional 1·3 life-years (95% CI 0·4 to 2·2) free of cardiovascular disease for SCORE and an additional 0·9 life-years (95% CI 0·5 to 1·3) for ASCVD. At age 65 years, this same improvement was associated with an additional 0·4 life-years (95% CI 0·0 to 0·7) free of cardiovascular disease for SCORE and 0·3 life-years (95% CI 0·1 to 0·5) for ASCVD. These models were developed into an interactive calculator, which enables estimation of the number of cardiovascular disease-free life-years for an individual as a function of two risk score measurements. Interpretation: Changes in the SCORE and ASCVD risk scores over time inform cardiovascular disease risk prediction beyond a single risk score assessment. Repeat data might allow more accurate cardiovascular risk stratification and strengthen the evidence base for decisions on preventive interventions. Funding: UK Medical Research Council, British Heart Foundation, Wellcome Trust, and US National Institute on Aging