Establishing the impact of COVID-19 on the health outcomes of domiciliary care workers in Wales using routine data: a protocol for the OSCAR study.
Establishing the impact of COVID-19 on the health outcomes of domiciliary care workers in Wales using routine data: a protocol for the OSCAR study.
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DOI：
10.23889/ijpds.v5i4.1656
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发表时间：
2020
影响因子：
--
通讯作者：
Robling M
中科院分区：
文献类型：
--
作者：
Lugg-Widger F;Cannings-John R;Akbari A;Brookes-Howell L;Hood K;John A;Jones H;Prout H;Schoenbuchner S;Thomas D;Robling M
在2019冠状病毒病大流行期间，家庭护理工作者继续在自己家中为成年人提供社会护理。关于COVID-19对DCWs健康结果影响的证据目前好坏参半，这可能反映了现有研究方法上的局限性。在人们家中提供护理的工作人员感染COVID-19的风险仍然未知。量化COVID-19对威尔士DCW健康结果的影响，探索变异的原因，并推断到英国其他DCW人口。混合方法设计，包括DCWs队列研究和探索性质的访谈。威尔士所有注册的DCWs的数据都可以通过SAIL数据库使用安全的、保护隐私的加密匿名过程获得。大流行期间工作的DCWs的职业登记数据将与SAIL数据库中的电子病历结果数据相结合，包括识别疑似和确诊COVID-19病例的临床代码。我们将报告疑似和确诊的COVID-19感染率以及包括死亡率在内的关键健康结果，并使用回归模型探索差异（按年龄、性别、种族、贫困五分位数、农村、雇主、合并症等因素），调整卫生局、地区和雇主内结果的聚类。将采用一种策略，对威尔士DCWs的最大变异样本进行定性访谈，该策略包括性别、年龄、种族和雇主等不同因素的参与者。访谈将为定量分析建模提供信息。我们将把定量研究结果推广到英国其他国家。OSCAR研究将使用匿名关联的职业和电子病历数据以及定性访谈，量化COVID-19对DCWs健康的风险，并探索变异的来源。这将为公共卫生政策和职业指导提供一个可靠的基础。
Domiciliary care workers (DCWs) continued providing social care to adults in their own homes throughout the COVID-19 pandemic. Evidence of the impact of COVID-19 on health outcomes of DCWs is currently mixed, probably reflecting methodological limitations of existing studies. The risk of COVID-19 to workers providing care in people’s homes remains unknown. To quantify the impact of COVID-19 upon health outcomes of DCWs in Wales, to explore causes of variation, and to extrapolate to the rest of the UK DCW population. Mixed methods design comprising cohort study of DCWs and exploratory qualitative interviews. Data for all registered DCWs in Wales is available via the SAIL Databank using a secured, privacy-protecting encrypted anonymisation process. Occupational registration data for DCWs working during the pandemic will be combined with EHR outcome data within the SAIL Databank including clinical codes that identify suspected and confirmed COVID-19 cases. We will report rates of suspected and confirmed COVID-19 infections and key health outcomes including mortality and explore variation (by factors such as age, sex, ethnicity, deprivation quintile, rurality, employer, comorbidities) using regression modelling, adjusting for clustering of outcome within Health Board, region and employer. A maximum variation sample of Welsh DCWs will be approached for qualitative interview using a strategy to include participants that vary across factors such as sex, age, ethnicity and employer. The interviews will inform the quantitative analysis modelling. We will generalise the quantitative findings to other UK nations. Using anonymised linked occupational and EHR data and qualitative interviews, the OSCAR study will quantify the risk of COVID-19 on DCWs’ health and explore sources of variation. This will provide a secure base for informing public health policy and occupational guidance.
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影响因子：
9.3
作者：
Niedzwiedz, Claire L.;O'Donnell, Kate A.;Katikireddi, S. Vittal
通讯作者：
Katikireddi, S. Vittal
影响因子：
3.5
作者：
Lyons RA;Jones KH;John G;Brooks CJ;Verplancke JP;Ford DV;Brown G;Leake K
通讯作者：
Leake K
影响因子：
4.5
作者：
Jones, Kerina H.;Ford, David V.;Jones, Chris;Dsilva, Rohan;Thompson, Simon;Brooks, Caroline J.;Heaven, Martin L.;Thayer, Daniel S.;McNerney, Cynthia L.;Lyons, Ronan A.
通讯作者：
Lyons, Ronan A.
影响因子：
4.9
作者：
Quinn MM;Markkanen PK;Galligan CJ;Sama SR;Kriebel D;Gore RJ;Brouillette NM;Okyere D;Sun C;Punnett L;Laramie AK;Davis L
通讯作者：
Davis L
影响因子：
--
作者：
Jones, K H;Ford, D V;Lyons, R A
通讯作者：
Lyons, R A