Acceptability of data linkage to identify women at risk of postnatal complication for the development of digital risk prediction tools and interventions to better optimise postnatal care, a qualitative descriptive study design.

BACKGROUND: Pregnancy acts as a cardiovascular stress test. Although many complications resolve following birth, women with hypertensive disorder of pregnancy have an increased risk of developing cardiovascular disease (CVD) long-term. Monitoring postnatal health can reduce this risk but requires better methods to identity high-risk women for timely interventions. METHODS: Employing a qualitative descriptive study design, focus groups and/or interviews were conducted, separately engaging public contributors and clinical professionals. Diverse participants were recruited through social media convenience sampling. Semi-structured, facilitator-led discussions explored perspectives of current postnatal assessment and attitudes towards linking patient electronic healthcare data to develop digital tools for identifying postpartum women at risk of CVD. Participant perspectives were gathered using post-it notes or a facilitator scribe and analysed thematically. RESULTS: From 27 public and seven clinical contributors, five themes regarding postnatal check expectations versus reality were developed, including 'limited resources', 'low maternal health priority', 'lack of knowledge', 'ineffective systems' and 'new mum syndrome'. Despite some concerns, all supported data linkage to identify women postnatally, targeting intervention to those at greater risk of CVD. Participants outlined potential benefits of digitalisation and risk prediction, highlighting design and communication needs for diverse communities. CONCLUSIONS: Current health system constraints in England contribute to suboptimal postnatal care. Integrating data linkage and improving education on data and digital tools for maternal healthcare shows promise for enhanced monitoring and improved future health. Recognised for streamlining processes and risk prediction, digital tools may enable more person-centred care plans, addressing the gaps in current postnatal care practice.

Heightened COVID-19 Mortality in People With Severe Mental Illness Persists After Vaccination: A Cohort Study of Greater Manchester Residents.

BACKGROUND AND HYPOTHESIS: Previous studies show that people with severe mental illness (SMI) are at higher risk of COVID-19 mortality, however limited evidence exists regarding risk postvaccination. We investigated COVID-19 mortality among people with schizophrenia and other SMIs before, during and after the UK vaccine roll-out. STUDY DESIGN: Using the Greater Manchester (GM) Care Record to access routinely collected health data linked with death records, we plotted COVID-19 mortality rates over time in GM residents with schizophrenia/psychosis, bipolar disorder (BD), and/or recurrent major depressive disorder (MDD) from February 2020 to September 2021. Multivariable logistic regression was used to compare mortality risk (risk ratios; RRs) between people with SMI (N = 193 435) and age-sex matched controls (N = 773 734), adjusted for sociodemographic factors, preexisting comorbidities, and vaccination status. STUDY RESULTS: Mortality risks were significantly higher among people with SMI compared with matched controls, particularly among people with schizophrenia/psychosis (RR 3.18, CI 2.94-3.44) and/or BD (RR 2.69, CI 2.16-3.34). In adjusted models, the relative risk of COVID-19 mortality decreased, though remained significantly higher than matched controls for people with schizophrenia (RR 1.61, CI 1.45-1.79) and BD (RR 1.92, CI 1.47-2.50), but not recurrent MDD (RR 1.08, CI 0.99-1.17). People with SMI continued to show higher mortality rate ratios relative to controls throughout 2021, during vaccination roll-out. CONCLUSIONS: People with SMI, notably schizophrenia and BD, were at greater risk of COVID-19 mortality compared to matched controls. Despite population vaccination efforts that have prioritized people with SMI, disparities still remain in COVID-19 mortality for people with SMI.

Findability of UK health datasets available for research: a mixed methods study.

OBJECTIVE: How health researchers find secondary data to analyse is unclear. We sought to describe the approaches that UK organisations take to help researchers find data and to assess the findability of health data that are available for research. METHODS: We surveyed established organisations about how they make data findable. We derived measures of findability based on the first element of the FAIR principles (Findable, Accessible, Interoperable, Reproducible). We applied these to 13 UK health datasets and measured their findability via two major internet search engines in 2018 and repeated in 2021. RESULTS: Among 12 survey respondents, 11 indicated that they made metadata publicly available. Respondents said internet presence was important for findability, but that this needed improvement. In 2018, 8 out of 13 datasets were listed in the top 100 search results of 10 searches repeated on both search engines, while the remaining 5 were found one click away from those search results. In 2021, this had reduced to seven datasets directly listed and one dataset one click away. In 2021, Google Dataset Search had become available, which listed 3 of the 13 datasets within the top 100 search results. DISCUSSION: Measuring findability via online search engines is one method for evaluating efforts to improve findability. Findability could perhaps be improved with catalogues that have greater inclusion of datasets, field-level metadata and persistent identifiers. CONCLUSION: UK organisations recognised the importance of the internet for finding data for research. However, health datasets available for research were no more findable in 2021 than in 2018.

Can Researchers Assess the Suitability of Datasets to Answer Their Research Questions, with Access to Metadata Only?

Health research increasingly requires effective ways to identify existing datasets and assess their suitability for research. We sought to test whether researchers could use an existing metadata catalogue to assess the suitability of datasets for addressing specified research questions. Five datasets were described in the National Institute for Health Research Health Informatics Collaborative metadata catalogue, and for each dataset five associated research questions were formulated, some of which were answerable with the dataset while others were not. Thirteen researchers each assessed whether the ten questions associated with two randomly selected datasets were answerable with the described datasets. After removing instances where participants misunderstood the question or lacked subject matter knowledge to make the assessment, we found that 87 out of 109 assessments (80%) were correct. Participants particularly struggled with one dataset which consisted of EHR data. The most common reason for incorrect assessments was the inability to find the relevant information in the metadata catalogue.

Temporal trends in primary care-recorded self-harm during and beyond the first year of the COVID-19 pandemic: Time series analysis of electronic healthcare records for 2.8 million patients in the Greater Manchester Care Record.

BACKGROUND: Surveillance of temporal trends in clinically treated self-harm is an important component of suicide prevention in the dynamic context of COVID-19. There is little evidence beyond the initial months following the onset of the pandemic, despite national and regional restrictions persisting to mid-2021. METHODS: Descriptive time series analysis utilizing de-identified, primary care health records of 2.8 million patients from the Greater Manchester Care Record. Frequencies of self-harm episodes between 1st January 2019 and 31st May 2021 were examined, including stratification by sex, age group, ethnicity, and index of multiple deprivation quintile. FINDINGS: There were 33,444 episodes of self-harm by 13,148 individuals recorded during the study period. Frequency ratios of incident and all episodes of self-harm were 0.59 (95% CI 0.51 to 0.69) and 0.69 (CI 0.63 to 0.75) respectively in April 2020 compared to February 2020. Between August 2020 and May 2021 frequency ratios were 0.92 (CI 0.88 to 0.96) for incident episodes and 0.86 (CI 0.84 to 0.88) for all episodes compared to the same months in 2019. Reductions were largest among men and people living in the most deprived neighbourhoods, while an increase in all-episode self-harm was observed for adolescents aged 10-17. INTERPRETATION: Reductions in primary care-recorded self-harm persisted to May 2021, though they were less marked than in April 2020 during the first national lockdown. The observed reductions could represent longer term reluctance to seek help from health services. Our findings have implications for the ability for services to offer recommended care for patients who have harmed themselves.