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BackgroundPOOL is a cohort study designed to establish whether waterbirth, compared to leaving a pool prior to birth, is as safe for mothers and infants. It is a novel case study using routine maternity and neonatal data for research purposes, including the adaptation and addition of locally collected electronic data at sites.
AimWe aimed to establish the feasibility of the study design, which combines data from maternity information systems(MIS) and neonatal data using a dissent-based linkage model and the addition of new variables into the MIS.
MethodsThe study will utilise individual level data entered into local MIS at 30 NHS Trusts linked with the National Neonatal Research Database(NNRD). New variables were added to one pilot site before rolling out to the remaining sites. A third party added the new variables, extracted data from the MIS to send to the study team and transferred data for matching to the NNRD, ensuring the study team receive only anonymised data. The feasibility study aimed to establish the quality and completion of the newly added variables, the levels of missingness for key outcomes from an extract of 4 years of retrospective data, and ensured the datasets could link using the study ID generated by our third party.
ResultsThe new variables were added into the MIS and data were extracted after a 10week pilot. New variables were present and had been entered as per specified metadata requirements. 23,140 records were extracted from the retrospective data and key variables assessed for missingness e.g.place of birth(<0.1% missing). The datasets could link using the studyID generated by the third party.
ConclusionThrough study set-up and in this pilot, we provide evidence that the main study is feasible, satisfies governance requirements and likely to generate data of sufficient quality to address our main research questions.

BackgroundUsing routinely collected clinical data for observational research is an increasingly important method for data collection, especially when rare outcomes are being explored. The POOL study was commissioned to evaluate the safety of waterbirth in the UK using routine maternity and neonatal clinical data. This paper describes the design, rationale, set-up and pilot for this data linkage study using bespoke methods.
MethodsClinical maternity information systems hold many data items of value for research purposes, but often lack specific data items required for individual studies. This study used the novel method of amending an existing clinical maternity database for the purpose of collecting additional research data fields. In combination with the extraction of existing data fields, this maximised the potential use of existing routinely collected clinical data for research purposes, whilst reducing NHS staff data collection burden.
Wellbeing Software® provider of the Euroking® Maternity Information System, added new study specific data fields to their information system, extracted data from participating NHS sites and transferred data for matching with the National Neonatal Research Database to ascertain outcomes for babies admitted to neonatal units. Study set-up processes were put in place for all sites. The data extraction, linkage and cleaning processes were piloted with one pre-selected NHS site.
ResultsTwenty-six NHS sites were set-up over 27 months (January 2019 - April 2021). Twenty-four thousand maternity records were extracted from the one NHS site, pertaining to the period January 2015 to March 2019. Data field completeness for maternal and neonatal primary outcomes were mostly acceptable. Neonatal identifiers flowed to the National Neonatal Research Database for successful matching and linkage between maternity and neonatal unit records.
DiscussionPiloting the data extraction and linkage highlighted the need for additional governance arrangements, training at NHS sites and new processes for the study team to ensure data quality and confidentiality are upheld during the study. Amending existing NHS electronic information systems and accessing clinical data at scale, is possible, but continues to be a time consuming and a technically challenging exercise.