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Precision public health: Mapping socioeconomic disparities in opioid dispensations at Swedish pharmacies by Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)

机译:精确的公共卫生:通过多级异质性和歧视性准确度进行多级分析,在瑞典药房映射瑞典药房的社会经理差异。(Maihda)

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摘要

BackgroundIn light of the opioid epidemic in the United States, there is growing concern about the use of opioids in Sweden as it may lead to misuse and overuse and, in turn, severe public health problems. However, little is known about the distribution of opioid use across different demographic and socioeconomic dimensions in the Swedish general population. Therefore, we applied an intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), to obtain an improved mapping of the risk heterogeneity of and socioeconomic inequalities in opioid prescription receipt.Methods and findingsUsing data from 6,846,106 residents in Sweden aged 18 and above, we constructed 72 intersectional strata from combinations of gender, age, income, cohabitation status, and presence or absence of psychological distress. We modelled the absolute risk (AR) of opioid prescription receipt in a series of multilevel logistic regression models distinguishing between additive and interaction effects. By means of the Variance Partitioning Coefficient (VPC) and the area under the receiver operating characteristic curve (AUC), we quantified the discriminatory accuracy (DA) of the intersectional strata for discerning those who received opioid prescriptions from those who did not. The AR of opioid prescription receipt ranged from 2.77% (95% CI 2.69-2.86) among low-income men aged 18-34, living alone, without psychological distress, to 28.25% (95% CI 27.95-28.56) among medium-income women aged 65 and older, living alone, with psychological distress. In a model that conflated both additive and interaction effects, the intersectional strata had a fair DA for discerning opioid users from non-users (VPC = 13.2%, AUC = 0.68). However, in the model that decomposed total effects into additive and interaction effects, the VPC was very low (0.42%) indicating the existence of small interaction effects for a number of the intersectional strata.ConclusionsThe intersectional MAIHDA approach aligns with the aims of precision public health, through improving the evidence base for health policy by increasing understanding of both health inequalities and individual heterogeneity. This approach is particularly relevant for socioeconomically conditioned outcomes such as opioid prescription receipt. We have identified intersections of social position within the Swedish population at greater risk for opioid prescription receipt.
机译:背景光线在美国的阿片类疫情,对瑞典的使用越来越担心,因为它可能导致滥用和过度使用,而且反过来严重的公共卫生问题。然而,关于瑞典一般人群中不同人口统计和社会经济尺寸的阿片类药物的分布很少。因此,我们应用了个性异质性和歧视性准确度(Maihda)的交叉多级分析,以获得阿片类药物处方收据中的风险异质性和社会经济不等式的改善映射。在18岁及以上瑞典的6,846,106名居民中,研究数据和发现数据我们从性别,年龄,收入,同居地位和存在或缺乏心理困扰的组合构建了72个交叉层。在区分添加剂和交互效应的一系列多级逻辑回归模型中,我们建模了阿片类药物处方收据的绝对风险(AR)。借助于方差分区系数(VPC)和接收器操作特性曲线(AUC)下的区域,我们量化了交叉阶层的歧视精度(DA),用于辨别那些从未提供的人接受那些来自那些人的阿片类药物处方。 Apioid处方收据的AR在18-34岁的低收入男性中,仅在18-34岁的低收入男性中,在没有心理痛苦的情况下,在中等收入的28.25%(95%CI 27.95-28.56)中,在28.25%(95%CI 27.95-28.56)之间65岁及以上的女性,独自生活,心理困扰。在混合添加剂和相互作用效果的模型中,交叉分层具有用于从非用户的OpioID用户探测OpioID用户的公平DA(VPC = 13.2%,AUC = 0.68)。然而,在将总效应的模型中分解成添加剂和相互作用效应,VPC非常低(0.42%),表明存在对多个交叉阶段的小相互作用效应的存在。结论次数的Maihda方法与Precision Public的目标对齐健康,通过提高对健康不平等和个人异质性的理解来改善卫生政策的证据基础。这种方法尤其与社会经济条件结果如阿片类药物处方收据相关。我们已经确定了瑞典人口内的社会地位,以更大的风险为表述处方收据。

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