Developing a synthetic national population to investigate the impact of different cardiovascular disease risk management strategies: A derivation and validation study

Background
Many national cardiovascular disease (CVD) risk factor management guidelines now recommend that drug treatment decisions should be informed primarily by patients’ multi-variable predicted risk of CVD, rather than on the basis of single risk factor thresholds. To investigate the potential impact of treatment guidelines based on CVD risk thresholds at a national level requires individual level data representing the multi-variable CVD risk factor profiles for a country’s total adult population. As these data are seldom, if ever, available, we aimed to create a synthetic population, representing the joint CVD risk factor distributions of the adult New Zealand population.
Methods and results
A synthetic population of 2,451,278 individuals, representing the actual age, gender, ethnicity and social deprivation composition of people aged 30–84 years who completed the 2013 New Zealand census was generated using Monte Carlo sampling. Each ‘synthetic’ person was then probabilistically assigned values of the remaining cardiovascular disease (CVD) risk factors required for predicting their CVD risk, based on data from the national census national hospitalisation and drug dispensing databases and a large regional cohort study, using Monte Carlo sampling and multiple imputation. Where possible, the synthetic population CVD risk distributions for each non-demographic risk factor were validated against independent New Zealand data sources.
Conclusions
We were able to develop a synthetic national population with realistic multi-variable CVD risk characteristics. The construction of this population is the first step in the development of a micro-simulation model intended to investigate the likely impact of a range of national CVD risk management strategies that will inform CVD risk management guideline updates in New Zealand and elsewhere.

研究背景
当前多国心血管疾病（cardiovascular disease, CVD）危险因素管理指南均建议，药物治疗决策应主要以患者的多变量心血管疾病预测风险为依据，而非单一危险因素阈值。若要在国家层面探究基于心血管疾病风险阈值的治疗指南的潜在影响，需获取能够反映一国全体成年人群多变量心血管疾病危险因素特征的个体水平数据。由于此类数据即便存在也极为稀缺，本研究旨在构建一组合成人群，用以反映新西兰成年人群的联合心血管疾病危险因素分布情况。
研究方法与结果
本研究采用蒙特卡洛抽样法，构建了包含2451278名个体的合成人群，该人群精准匹配2013年新西兰人口普查中30~84岁人群的实际年龄、性别、种族及社会剥夺构成。随后，基于全国人口普查、全国住院登记与药品配发数据库及一项大型区域队列研究的数据，本研究通过蒙特卡洛抽样与多重插补法，为每一位"合成"个体概率性赋值预测心血管疾病风险所需的其余心血管疾病危险因素数值。在条件允许的前提下，本研究针对每一项非人口学危险因素，将合成人群的心血管疾病风险分布与新西兰独立数据源进行了比对验证。
研究结论
本研究成功构建了具备真实多变量心血管疾病风险特征的全国合成人群。该合成人群的构建是开发微观模拟模型的首要步骤，该模型旨在探究一系列国家级心血管疾病风险管理策略的潜在影响，可为新西兰及全球其他地区的心血管疾病风险管理指南更新提供科学依据。