An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data.
An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data.
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DOI：
10.1038/s41598-023-34011-3
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发表时间：
2023-05-19
影响因子：
4.6
通讯作者：
Naughton, Aideen
中科院分区：
文献类型：
--
作者：
John, Ann;McGregor, Joanna;Marchant, Amanda;DelPozo-Banos, Marcos;Farr, Ian;Nurmatov, Ulugbek;Kemp, Alison;Naughton, Aideen
需要在初级和二级保健数据中确定儿童虐待（CM）的有效方法。我们的目标是创建第一个外部验证的算法，用于使用常规收集的医疗数据识别虐待。斯旺西大学SAIL数据库中创建了全面的代码列表，供全科医生和医院入院数据集使用，与保护临床医生和学者合作。这些代码列表建立在以前发布的代码列表的基础上，并对其进行了改进，以包括一组详尽的代码。敏感性，特异性和阳性预测值的先前公布的名单和新的算法进行了估计对临床评估队列的CM情况下，从儿童保护服务二级保健为基础的设置-“金标准”。我们进行了敏感性分析，以检查指示可能CM的更广泛代码的实用性。使用泊松回归模型计算了2004年至2020年的趋势。我们的算法优于先前发表的列表，识别了43-72%的初级保健病例，特异性≥ 85%。在入院数据中识别虐待的算法的灵敏度较低，识别出9%至28%的高特异性病例（> 96%）。手动检索外部数据集识别但未在初级保健中记录的病例的记录表明，该代码列表是详尽的。对遗漏病例的探索表明，入院数据往往侧重于正在接受治疗的伤害，而不是记录虐待的存在。入院数据中缺乏儿童保护或社会关怀守则，这对在入院数据中识别虐待行为造成了限制。将全科医生和医院入院联系起来，可以最大限度地提高可以准确识别的虐待案件的数量。随着时间的推移，使用这些代码清单的初级保健中的虐待发生率有所增加。更新后的算法提高了我们在常规收集的医疗保健数据中检测CM的能力。重要的是要认识到在个人医疗数据集中识别虐待的局限性。在初级保健数据中纳入儿童保护代码使其成为识别CM的重要设置，而入院数据通常侧重于CM代码通常缺失的伤害。未来的研究算法的影响和效用进行了讨论。
Validated methods of identifying childhood maltreatment (CM) in primary and secondary care data are needed. We aimed to create the first externally validated algorithm for identifying maltreatment using routinely collected healthcare data. Comprehensive code lists were created for use within GP and hospital admissions datasets in the SAIL Databank at Swansea University working with safeguarding clinicians and academics. These code lists build on and refine those previously published to include an exhaustive set of codes. Sensitivity, specificity and positive predictive value of previously published lists and the new algorithm were estimated against a clinically assessed cohort of CM cases from a child protection service secondary care-based setting—‘the gold standard’. We conducted sensitivity analyses to examine the utility of wider codes indicating Possible CM. Trends over time from 2004 to 2020 were calculated using Poisson regression modelling. Our algorithm outperformed previously published lists identifying 43–72% of cases in primary care with a specificity ≥ 85%. Sensitivity of algorithms for identifying maltreatment in hospital admissions data was lower identifying between 9 and 28% of cases with high specificity (> 96%). Manual searching of records for those cases identified by the external dataset but not recorded in primary care suggest that this code list is exhaustive. Exploration of missed cases shows that hospital admissions data is often focused on the injury being treated rather than recording the presence of maltreatment. The absence of child protection or social care codes in hospital admissions data poses a limitation for identifying maltreatment in admissions data. Linking across GP and hospital admissions maximises the number of cases of maltreatment that can be accurately identified. Incidence of maltreatment in primary care using these code lists has increased over time. The updated algorithm has improved our ability to detect CM in routinely collected healthcare data. It is important to recognize the limitations of identifying maltreatment in individual healthcare datasets. The inclusion of child protection codes in primary care data makes this an important setting for identifying CM, whereas hospital admissions data is often focused on injuries with CM codes often absent. Implications and utility of algorithms for future research are discussed.
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影响因子：
3.7
作者：
Woodman J;Freemantle N;Allister J;de Lusignan S;Gilbert R;Petersen I
通讯作者：
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影响因子：
5.4
作者：
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作者：
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通讯作者：
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4.5
作者：
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通讯作者：
Scott, Debbie A.