The impact of COVID-19 on population health is recognised as being substantial, yet few studies have attempted to quantify to what extent infection causes mild or moderate symptoms only, requires... Show moreThe impact of COVID-19 on population health is recognised as being substantial, yet few studies have attempted to quantify to what extent infection causes mild or moderate symptoms only, requires hospital and/or ICU admission, results in prolonged and chronic illness, or leads to premature death. We aimed to quantify the total disease burden of acute COVID-19 in the Netherlands in 2020 using the disability-adjusted life-years (DALY) measure, and to investigate how burden varies between age-groups and occupations. Using standard methods and diverse data sources (mandatory notifications, population-level seroprevalence, hospital and ICU admissions, registered COVID-19 deaths, and the literature), we estimated years of life lost (YLL), years lived with disability, DALY and DALY per 100,000 population due to COVID-19, excluding post-acute sequelae, stratified by 5-year age-group and occupation category. The total disease burden due to acute COVID-19 was 286,100 (95% CI: 281,700-290,500) DALY, and the per-capita burden was 1640 (95% CI: 1620-1670) DALY/100,000, of which 99.4% consisted of YLL. The per-capita burden increased steeply with age, starting from 60 to 64 years, with relatively little burden estimated for persons under 50 years old. SARS-CoV-2 infection and associated premature mortality was responsible for a considerable direct health burden in the Netherlands, despite extensive public health measures. DALY were much higher than for other high-burden infectious diseases, but lower than estimated for coronary heart disease. These findings are valuable for informing public health decision-makers regarding the expected COVID-19 health burden among population subgroups, and the possible gains from targeted preventative interventions. Show less
BackgroundPreeclampsia is a female-specific risk factor for the development of future cardiovascular disease. Whether early preventive cardiovascular disease risk screenings combined with risk... Show moreBackgroundPreeclampsia is a female-specific risk factor for the development of future cardiovascular disease. Whether early preventive cardiovascular disease risk screenings combined with risk-based lifestyle interventions in women with previous preeclampsia are beneficial and cost-effective is unknown.MethodsA micro-simulation model was developed to assess the life-long impact of preventive cardiovascular screening strategies initiated after women experienced preeclampsia during pregnancy. Screening was started at the age of 30 or 40 years and repeated every five years. Data (initial and follow-up) from women with a history of preeclampsia was used to calculate 10-year cardiovascular disease risk estimates according to Framingham Risk Score. An absolute risk threshold of 2% was evaluated for treatment selection, i.e. lifestyle interventions (e.g. increasing physical activity). Screening benefits were assessed in terms of costs and quality-adjusted-life-years, and incremental cost-effectiveness ratios compared with no screening.ResultsExpected health outcomes for no screening are 27.35 quality-adjusted-life-years and increase to 27.43 quality-adjusted-life-years (screening at 30 years with 2% threshold). The expected costs for no screening are euro9426 and around euro13,881 for screening at 30 years (for a 2% threshold). Preventive screening at 40 years with a 2% threshold has the most favourable incremental cost-effectiveness ratio, i.e. euro34,996/quality-adjusted-life-year, compared with other screening scenarios and no screening.ConclusionsEarly cardiovascular disease risk screening followed by risk-based lifestyle interventions may lead to small long-term health benefits in women with a history of preeclampsia. However, the cost-effectiveness of a lifelong cardiovascular prevention programme starting early after preeclampsia with risk-based lifestyle advice alone is relatively unfavourable. A combination of risk-based lifestyle advice plus medical therapy may be more beneficial. Show less
Objectives Compare the predictive performance of Framingham Risk Score (FRS), Pooled Cohort Equations (PCEs) and Systematic COronary Risk Evaluation (SCORE) model between women with and without a... Show moreObjectives Compare the predictive performance of Framingham Risk Score (FRS), Pooled Cohort Equations (PCEs) and Systematic COronary Risk Evaluation (SCORE) model between women with and without a history of hypertensive disorders of pregnancy (hHDP) and determine the effects of recalibration and refitting on predictive performance. Methods We included 29 751 women, 6302 with hHDP and 17 369 without. We assessed whether models accurately predicted observed 10-year cardiovascular disease (CVD) risk (calibration) and whether they accurately distinguished between women developing CVD during follow-up and not (discrimination), separately for women with and without hHDP. We also recalibrated (updating intercept and slope) and refitted (recalculating coefficients) the models. Results Original FRS and PCEs overpredicted 10-year CVD risks, with expected:observed (E:O) ratios ranging from 1.51 (for FRS in women with hHDP) to 2.29 (for PCEs in women without hHDP), while E:O ratios were close to 1 for SCORE. Overprediction attenuated slightly after recalibration for FRS and PCEs in both hHDP groups. Discrimination was reasonable for all models, with C-statistics ranging from 0.70-0.81 (women with hHDP) and 0.72-0.74 (women without hHDP). C-statistics improved slightly after refitting 0.71-0.83 (with hHDP) and 0.73-0.80 (without hHDP). The E:O ratio of the original PCE model was statistically significantly better in women with hHDP compared with women without hHDP. Conclusions SCORE performed best in terms of both calibration and discrimination, while FRS and PCEs overpredicted risk in women with and without hHDP, but improved after recalibrating and refitting the models. No separate model for women with hHDP seems necessary, despite their higher baseline risk. Show less
AimTo provide a comprehensive overview of cardiovascular disease (CVD) risk prediction models for women and models that include female-specific predictors.MethodsWe performed a systematic review of... Show moreAimTo provide a comprehensive overview of cardiovascular disease (CVD) risk prediction models for women and models that include female-specific predictors.MethodsWe performed a systematic review of CVD risk prediction models for women in the general population by updating a previous review. We searched Medline and Embase up to July 2017 and included studies in which; (a) a new model was developed, (b) an existing model was validated, or (c) a predictor was added to an existing model.ResultsA total of 285 prediction models for women have been developed, of these 160 (56%) were female-specific models, in which a separate model was developed solely in women and 125 (44%) were sex-predictor models. Out of the 160 female-specific models, 2 (1.3%) included one or more female-specific predictors (mostly reproductive risk factors). A total of 591 validations of sex-predictor or female-specific models were identified in 206 papers. Of these, 333 (56%) validations concerned nine models (five versions of Framingham, SCORE, Pooled Cohort Equations and QRISK). The median and pooled C statistics were comparable for sex-predictor and female-specific models. In 260 articles the added value of new predictors to an existing model was described, however in only 3 of these female-specific that no competing interests exist. predictors (reproductive risk factors) were added.ConclusionsThere is an abundance of models for women in the general population. Female-specific and sex-predictor models have similar predictors and performance. Female-specific predictors are rarely included. Further research is needed to assess the added value of female-specific predictors to CVD models for women and provide physicians with a well-performing prediction model for women. Show less
