Background and ObjectivesFemale-specific factors and psychosocial factors may be important in the prediction of strokebut are not included in prediction models that are currently used. We... Show moreBackground and ObjectivesFemale-specific factors and psychosocial factors may be important in the prediction of strokebut are not included in prediction models that are currently used. We investigated whetheraddition of these factors would improve the performance of prediction models for the risk ofstroke in women younger than 50 years.MethodsWe used data from the Stichting Informatievoorziening voor Zorg en Onderzoek, population-based, primary care database of women aged 20–49 years without a history of cardiovasculardisease. Analyses were stratified by 10-year age intervals at cohort entry. Cox proportionalhazards models to predict stroke risk were developed, including traditional cardiovascularfactors, and compared with models that additionally included female-specific and psychosocialfactors. We compared the risk models using the c-statistic and slope of the calibration curve at afollow-up of 10 years. We developed an age-specific stroke risk prediction tool that may helpcommunicating the risk of stroke in clinical practice.ResultsWe included 409,026 women with a total of 3,990,185 person-years of follow-up. Strokeoccurred in 2,751 women (incidence rate 6.9 [95% CI 6.6–7.2] per 10,000 person-years).Models with only traditional cardiovascular factors performed poorly to moderately in all agegroups: 20–29 years: c-statistic: 0.617 (95% CI 0.592–0.639); 30–39 years: c-statistic: 0.615(95% CI 0.596–0.634); and 40–49 years: c-statistic: 0.585 (95% CI 0.573–0.597). After addingthe female-specific and psychosocial risk factors to the reference models, the model discrimi-nation increased moderately, especially in the age groups 30–39 (Dc-statistic: 0.019) and 40–49years (Dc-statistic: 0.029) compared with the reference models, respectively.DiscussionThe addition of female-specific factors and psychosocial risk factors improves the discrimina-tory performance of prediction models for stroke in women younger than 50 years. Show less
Smoorenburg, S. van; Kist, J.M.; Vos, R.C.; Vos, H.M.M. 2023
Improving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional... Show moreImproving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional cross-domain partnerships have emerged in several countries, which aim to achieve better population health, quality of care and a reduction in the per capita costs. These cross-domain partnerships aim to have a strong data foundation and are committed to continuous learning in which data plays an essential role. This paper describes our approach towards the development of the regional integrative population-based data infrastructure Extramural LUMC (Leiden University Medical Center) Academic Network (ELAN), in which we linked routinely collected medical, social and public health data at the patient level from the greater The Hague and Leiden area. Furthermore, we discuss the methodological issues of routine care data and the lessons learned about privacy, legislation and reciprocities. The initiative presented in this paper is relevant for international researchers and policy-makers because a unique data infrastructure has been set up that contains data across different domains, providing insights into societal issues and scientific questions that are important for data driven population health management approaches. Show less
Improving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional... Show moreImproving population health and reducing inequalities through better integrated health and social care services is high up on the agenda of policymakers internationally. In recent years, regional cross-domain partnerships have emerged in several countries, which aim to achieve better population health, quality of care and a reduction in the per capita costs. These cross-domain partnerships aim to have a strong data foundation and are committed to continuous learning in which data plays an essential role. This paper describes our approach towards the development of the regional integrative population-based data infrastructure Extramural LUMC (Leiden University Medical Center) Academic Network (ELAN), in which we linked routinely collected medical, social and public health data at the patient level from the greater The Hague and Leiden area. Furthermore, we discuss the methodological issues of routine care data and the lessons learned about privacy, legislation and reciprocities. The initiative presented in this paper is relevant for international researchers and policy-makers because a unique data infrastructure has been set up that contains data across different domains, providing insights into societal issues and scientific questions that are important for data driven population health management approaches. Show less
Background Socioeconomic status and ethnicity are not explicitly incorporated as risk factors in the four SCORE2 cardiovascular disease (CVD) risk models developed for country-wide implementation... Show moreBackground Socioeconomic status and ethnicity are not explicitly incorporated as risk factors in the four SCORE2 cardiovascular disease (CVD) risk models developed for country-wide implementation across Europe (low, moderate, high and very-high model). The aim of this study was to evaluate the performance of the four SCORE2 CVD risk prediction models in an ethnic and socioeconomic diverse population in the Netherlands.Methods The SCORE2 CVD risk models were externally validated in socioeconomic and ethnic (by country of origin) subgroups, from a population-based cohort in the Netherlands, with GP, hospital and registry data. In total 155,000 individuals, between 40 and 70 years old in the study period from 2007 to 2020 and without previous CVD or diabetes were included. Variables (age, sex, smoking status, blood pressure, cholesterol) and outcome first CVD event (stroke, myocardial infarction, CVD death) were consistent with SCORE2. Findings 6966 CVD events were observed, versus 5495 events predicted by the CVD low-risk model (intended for use in the Netherlands). Relative underprediction was similar in men and women (observed/predicted (OE-ratio), 1.3 and 1.2 in men and women, respectively). Underprediction was larger in low socioeconomic subgroups of the overall study population (OE-ratio 1.5 and 1.6 in men and women, respectively), and comparable in Dutch and the combined "other ethnicities" low socioeconomic subgroups. Underprediction in the Surinamese subgroup was largest (OE-ratio 1.9, in men and women), particularly in the low socioeconomic Surinamese subgroups (OE-ratio 2.5 and 2.1 in men and women). In the subgroups with underprediction in the low-risk model, the intermediate or high-risk SCORE2 models showed improved OE-ratios. Discrimination showed moderate performance in all subgroups and the four SCORE2 models, with C-statistics between 0.65 and 0.72, similar to the SCORE2 model development study.Interpretation The SCORE 2 CVD risk model for low-risk countries (as the Netherlands are) was found to underpredict CVD risk, particularly in low socioeconomic and Surinamese ethnic subgroups. Including socioeconomic status and ethnicity as predictors in CVD risk models and implementing CVD risk adjustment within countries is desirable for adequate CVD risk prediction and counselling. Show less
Background: Socioeconomic status and ethnicity are not incorporated as predictors in country-level cardiovascular risk charts on mainland Europe. The aim of this study was to quantify the sex... Show moreBackground: Socioeconomic status and ethnicity are not incorporated as predictors in country-level cardiovascular risk charts on mainland Europe. The aim of this study was to quantify the sex-specific cardiovascular death rates stratified by ethnicity and socioeconomic factors in an urban population in a universal healthcare system. Methods: Age-standardized death rates (ASDR) were estimated in a dynamic population, aged 45-75 in the city of The Hague, the Netherlands, over the period 2007-2018, using data of Statistics Netherlands. Results were stratified by sex, ethnicity (country of birth) and socioeconomic status (prosperity) and compared with a European cut-off for high-risk countries (ASDR men 225/100,000 and women 175/100,000). Findings: In total, 3073 CVD deaths occurred during 1 cent 76 million person years follow-up. Estimated ASDRs (selected countries of birth) ranged from 126 (95%CI 89-174) in Moroccan men to 379 (95%CI 272-518) in Antillean men, and from 86 (95%CI 50-138) in Moroccan women to 170 (95%CI 142-202) in Surinamese women. ASDRs in the highest and lowest prosperity quintiles were 94 (95%CI 90-98) and 343 (95%CI 334-351) for men, and 43 (95%CI 41-46) and 140 (95%CI 135-145), for women, respectively. Interpretation: In a diverse urban population, large health disparities in cardiovascular ASDRs exists across ethnic and socioeconomic subgroups. Identifying these high-risk subgroups followed by targeted preventive efforts, might provide a basis for improving cardiovascular health equity within communities. Instead of classifying countries as high-risk or low-risk, a shift towards focusing on these subgroups within countries might be needed. Funding: Leiden University Medical Center and Leiden University (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) Show less
