Non-Communicable Diseases Research Centre, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Monash University Accident Research Centre, Monash University, Clayton, Victoria, Australia.
PLoS One. 2021 Feb 4;16(2):e0246253. doi: 10.1371/journal.pone.0246253. eCollection 2021.
Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting about 1.6% of the population in England. Novel oral anticoagulants (NOACs) are approved AF treatments that reduce stroke risk. In this study, we estimate the equality in individual NOAC prescriptions with high spatial resolution in Clinical Commissioning Groups (CCGs) across England from 2014 to 2019.
A Bayesian spatio-temporal model will be used to estimate and predict the individual NOAC prescription trend on 'prescription data' as an indicator of health services utilisation, using a small area analysis methodology. The main dataset in this study is the "Practice Level Prescribing in England," which contains four individual NOACs prescribed by all registered GP practices in England. We will use the defined daily dose (DDD) equivalent methodology, as recommended by the World Health Organization (WHO), to compare across space and time. Four licensed NOACs datasets will be summed per 1,000 patients at the CCG-level over time. We will also adjust for CCG-level covariates, such as demographic data, Multiple Deprivation Index, and rural-urban classification. We aim to employ the extended BYM2 model (space-time model) using the RStan package.
This study suggests a new statistical modelling approach to link prescription and socioeconomic data to model pharmacoepidemiologic data. Quantifying space and time differences will allow for the evaluation of inequalities in the prescription of NOACs. The methodology will help develop geographically targeted public health interventions, campaigns, audits, or guidelines to improve areas of low prescription. This approach can be used for other medications, especially those used for chronic diseases that must be monitored over time.
心房颤动（AF）是最常见的心律失常，影响英国约 1.6%的人口。新型口服抗凝剂（NOAC）是批准的 AF 治疗方法，可降低中风风险。在这项研究中，我们使用贝叶斯时空模型，使用小区域分析方法，从 2014 年到 2019 年，在英格兰的临床委托组（CCG）中以高空间分辨率估计和预测个体 NOAC 处方的平等性。
将使用贝叶斯时空模型来估计和预测“处方数据”中的个体 NOAC 处方趋势，作为卫生服务利用的指标，使用小区域分析方法。本研究的主要数据集是“英格兰的实践水平处方”，其中包含英格兰所有注册全科医生实践中规定的四种个体 NOAC。我们将使用世界卫生组织（WHO）推荐的定义日剂量（DDD）等效方法进行比较。将在每个 CCG 水平上，将四个许可的 NOAC 数据集在时间上汇总为每 1000 名患者。我们还将调整 CCG 水平的协变量，如人口统计数据、多重剥夺指数和城乡分类。我们的目标是使用 RStan 包使用扩展的 BYM2 模型（时空模型）。
本研究提出了一种新的统计建模方法，将处方和社会经济数据联系起来，以对药物流行病学数据进行建模。量化空间和时间差异将有助于评估 NOAC 处方的不平等。该方法将有助于制定针对地理区域的公共卫生干预、宣传、审计或指南，以改善处方率低的地区。这种方法可用于其他药物，特别是那些用于需要随时间监测的慢性疾病的药物。
