Jane Lyons

Background: Childhood residential mobility (RM) has been associated with adverse health and social outcomes. We examined differences in RM by family size and household composition in two diverse, disadvantaged UK populations. Methods: Using electronic health records (EHRs) and anonymised addresses, we examined if household characteristics are associated with RM in north-east London (NEL) and Wales. We conducted a longitudinal study of 240,509 (51.0 per cent male) and 398,959 (51.3 per cent male) children aged 0–13 years in NEL and Wales respectively on 1 January 2015, with complete follow-up until 31 December 2019. We defined RM as at least one change in address during follow-up. We estimated odds ratios (OR) and 95 per cent confidence intervals (CI) for RM by interactions between age group (0–4, 5–9, 10–13 years), number of children in household and household composition, adjusting for demographic, household and area-level characteristics. Results: Overall, 20.6 per cent and 32.1 per cent of children experienced RM. RM was highest in the youngest age group: NEL: 25.3 per cent, 18.7 per cent, 15.6 per cent; Wales: 40.1 per cent, 29.4 per cent, 23.6 per cent. RM was more likely among 10–13-year-olds living with a single adult than two working-age adults (NEL OR: 1.15; 95 per cent CI: 1.07,1.25; Wales: 1.82; 1.73,1.91). Children in households with four or more children were more likely to experience RM than those with no other child among 10–13-year-olds in NEL (1.31; 1.18,1.46) and 5–9-year-olds and 10–13-year-olds in Wales (1.21; 1.14,1.29 and 1.68; 1.56,1.82). Conclusion: RM is more common in early childhood and varies by household composition, region and age group. EHRs can be used to estimate RM using real-world data and have the potential to examine its impact on health and other outcomes.

Background and ObjectiveHealthcare research faces challenges in developing metrics that resonate with the general public or policymakers. We created a Desirable Health Indicator (DHI) to address this gap, centred around New Year's wishes for survival and non-occurrence of undesired events in the following year, for the population of Wales, UK, following discussions with policymakers and members of the public. MethodsWe created retrospective, population-based individual-level cohorts from linked routinely collected anonymised, health and demographic data from the Secure Anonymised Information Linkage (SAIL) Databank (2015-2022). The DHI was calculated per person per year and quantified the distribution of the population who survive calendar years and do not use selected health services (not admitted to hospital; no emergency department attendance; and not prescribed medication used in infection, analgesics, or mental health drugs). Group and individual interviews were held with members of the public and policy makers seeking their views of the indicator. ResultsThe findings were understood and well received by members of the general public and policymakers. Between 2015 and 2019, the percentage of individuals meeting the DHI ranged between 39.6%-41.9%, increasing to 48.6% and 46.2% for2020 and 2021respectively, and reducing to 43.1% in 2022. Focussing on the year 2022, 1,154,630 (43.1%) met the DHI from a population of 2,677,829. The percentage of people with desirable health decreased significantly with age and with increasing socioeconomic deprivation. A higher proportion of males (49.2%) met the DHI compared to females (37.1%). Being male (aOR = 1.62 [95%CI 1.61,1.63]), 10-19 years of age (aOR = 1.69 [95%CI 1.68,1.71]), and living in the least deprived areas of Wales (aOR = 1.31 [95%CI 1.30,1.32]) were the characteristics associated with the highest odds of meeting the desirable health indicator. The most prevalent reasons for not meeting the indicator were GP prescriptions for drugs used in infections (29.5%), analgesics (22.8%) and mental health conditions (20.2%). ConclusionThe DHI provides an insightful and novel tool for monitoring aspects of population health and healthcare utilisation. The DHI's coverage of important topics, derived from routine data sources, makes it a reproducible, temporally flexible, and easily understood indicator, suitable for informing policy development and addressing aspects of health inequalities. As data linkage capabilities expand internationally there are opportunities for implementation to aid comparison and better understanding of how systems perform.

Background: The COVID-19 pandemic resulted in increased mortality directly and indirectly associated with COVID-19. Aim: To assess the impact of the COVID-19 pandemic on all-cause and disease-specific mortality and explore potential health inequalities associated with area-level deprivation in Wales. Methods: Two population-based cohort studies were derived from multi-sourced, linked demographic, administrative and electronic health record data from 2016 to 2019 (n = 3,113,319) and 2020 to 2022 (n = 3,571,471). Data were analysed using generalised linear models adjusting for age, sex, area-level deprivation and time at risk. Results: COVID-19 deaths peaked in January 2021 (54.9/100,000 person-months, 95% confidence interval (CI): 52.4–57.5). The pandemic indirectly affected deaths, with higher than expected maximum relative mortality rates (RR) related to cancer (RR: 1.24, 95% CI: 1.13–1.36), infectious diseases (excluding respiratory infections) (RR: 2.09, 95% CI: 1.27– 3.43), circulatory system (RR: 1.41, 95% CI: 1.28–1.56), trauma (RR: 2.04, 95% CI: 1.57– 2.65), digestive system (RR: 1.54, 95% CI: 1.25–1.91), nervous system (RR: 1.63; 95% CI: 1.34–2.00) and mental and behavioural disorders (RR: 1.85, 95% CI: 1.58–2.16). Mortality associated with respiratory diseases (unrelated to COVID-19) were lower than expected (minimum RR: 0.52, 95% CI: 0.45–0.60). All-cause mortality was lower in least deprived communities compared with most deprived (RR: 0.61, 95% CI: 0.60–0.62), and the magnitude of this effect increased during the pandemic. Conclusions: All-cause and disease-specific mortality directly and indirectly associated with COVID-19 increased during the COVID-19 pandemic. Socioeconomic disparities were exacerbated during this time.

Objective This study aims to create a national ethnicity spine based on all available ethnicity records in linkable anonymised electronic health record and administrative data sources. Design A longitudinal study using anonymised individual-level population-scale ethnicity data from 26 data sources available within the Secure Anonymised Information Linkage Databank. Setting The national ethnicity spine is created based on longitudinal national data for the population of Wales-UK over 22 years (between 2000 and 2021). Procedure and participants A total of 46 million ethnicity records for 4 297 694 individuals have been extracted, harmonised, deduplicated and made available within a longitudinal research ready data asset. Outcome measures (1) Comparing the distribution of ethnicity records over time for four different selection approaches (latest, mode, weighted mode and composite) across age bands, sex, deprivation quintiles, health board and residential location and (2) distribution and completeness of records against the ONS census 2011. Results The distribution of the dominant group (white) is minimally affected based on the four different selection approaches. Across all other ethnic group categorisations, the mixed group was most susceptible to variation in distribution depending on the selection approach used and varied from a 0.6% prevalence across the latest and mode approach to a 1.1% prevalence for the weighted mode, compared with the 3.1% prevalence for the composite approach. Substantial alignment was observed with ONS 2011 census with the Latest group method (kappa=0.68, 95% CI (0.67 to 0.71)) across all subgroups. The record completeness rate was over 95% in 2021. Conclusion In conclusion, our development of the population-scale ethnicity spine provides robust ethnicity measures for healthcare research in Wales and a template which can easily be deployed in other trusted research environments in the UK and beyond.

Objectives To compare the patterns of multimorbidity between people with and without rheumatic and musculoskeletal diseases (RMDs) and to describe how these patterns change by age and sex over time, between 2010 and 2019. Participants 103 426 people with RMDs and 2.9 million comparators registered in 395 Wales general practices (GPs). Each patient with an RMD aged 0–100 years between January 2010 and December 2019 registered in Clinical Practice Research Welsh practices was matched with up to five comparators without an RMD, based on age, gender and GP code. Primary outcome measures The prevalence of 29 Elixhauser-defined comorbidities in people with RMDs and comparators categorised by age, gender and GP practices. Conditional logistic regression models were fitted to calculate differences (OR, 95% CI) in associations with comorbidities between cohorts. Results The most prevalent comorbidities were cardiovascular risk factors, hypertension and diabetes. Having an RMD diagnosis was associated with a significantly higher odds for many conditions including deficiency anaemia (OR 1.39, 95% CI (1.32 to 1.46)), hypothyroidism (OR 1.34, 95% CI (1.19 to 1.50)), pulmonary circulation disorders (OR 1.39, 95% CI 1.12 to 1.73) diabetes (OR 1.17, 95% CI (1.11 to 1.23)) and fluid and electrolyte disorders (OR 1.27, 95% CI (1.17 to 1.38)). RMDs have a higher proportion of multimorbidity (two or more conditions in addition to the RMD) compared with non-RMD group (81% and 73%, respectively in 2019) and the mean number of comorbidities was higher in women from the age of 25 and 50 in men than in non-RMDs group. Conclusion People with RMDs are approximately 1.5 times as likely to have multimorbidity as the general population and provide a high-risk group for targeted intervention studies. The individuals with RMDs experience a greater load of coexisting health conditions, which tend to manifest at earlier ages. This phenomenon is particularly pronounced among women. Additionally, there is an under-reporting of comorbidities in individuals with RMDs.

Background: Multimorbidity is one of the greatest challenges facing health and social care systems globally. It is associated with high rates of health service use, adverse healthcare events, and premature death. Despite its importance, little is known about the effects of contextual determinants such as household and area characteristics on health and care outcomes for people with multimorbidity. This study protocol presents a plan for the examination of associations between individual, household, and area characteristics with important health and social care outcomes. Methods: The study will use a cross-section of data from the SAIL Databank on 01 January 2019 and include all people alive and registered with a Welsh GP. The cohort will be stratified according to the presence or absence of multimorbidity, defined as two or more long-term conditions. Multilevel models will be used to examine covariates measured for individuals, households, and areas to account for social processes operating at different levels. The intra-class correlation coefficient will be calculated to determine the strength of association at each level of the hierarchy. Model outcomes will be any emergency department attendance, emergency hospital or care home admission, or mortality, within the study follow-up period. Discussion: Household and area characteristics might act as protective or risk factors for health and care outcomes for people with multimorbidity, in which case results of the analyses can be used to guide clinical and policy responses for effective targeting of limited resources.

Introduction: At the start of the COVID-19 pandemic there was an urgent need to identify individuals at highest risk of severe outcomes, such as hospitalisation and death following infection. The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed during the second wave of the COVID-19 pandemic to identify groups of people at highest risk of severe COVID-19 related outcomes following one or two doses of vaccine. Objectives: To externally validate the QCOVID3 algorithm based on primary and secondary care records for Wales, UK. Methods: We conducted an observational, prospective cohort based on electronic health care records for 1.66m vaccinated adults living in Wales on 8^th December 2020, with follow-up until 15^th June 2021. Follow-up started from day 14 post vaccination to allow the full effect of the vaccine. Results: The scores produced by the QCOVID3 risk algorithm showed high levels of discrimination for both COVID-19 related deaths and hospital admissions and good calibration (Harrell C statistic: ≥ 0.828). Conclusion: This validation of the updated QCOVID3 risk algorithms in the adult vaccinated Welsh population has shown that the algorithms are valid for use in the Welsh population, and applicable on a population independent of the original study, which has not been previously reported. This study provides further evidence that the QCOVID algorithms can help inform public health risk management on the ongoing surveillance and intervention to manage COVID-19 related risks.

Background Multimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include. Methods and Findings We conducted a cross-sectional study using English primary care data for 1168260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity when varying the number and selection of conditions considered (≥two conditions) for 80 conditions. Included conditions featured in ≥one of the nine published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common two conditions, three conditions, etc, up to 80 conditions. Second, prevalence was calculated using nine condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the two commonest conditions were considered was 4.6% (95%CI [4.6,4.6] p 80 years) and higher in younger people (71 in 0–9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of ‘comorbidity’. Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition lists, but this highlights further variability in prevalence estimates across studies. Conclusions In this study we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence. Why was this study done? There is wide variety in the conditions considered by researchers when measuring multimorbidity prevalence. A systematic review of 566 studies, published in 2021, found lack of consensus in the selection of conditions considered. In half of studies only eight conditions (diabetes, stroke, cancer, chronic obstructive pulmonary disease, hypertension, coronary heart disease, chronic kidney disease, and heart failure) were consistently considered; and the number of conditions considered varied from 2 to 285 (median 17). A more consistent approach to measuring multimorbidity is needed to facilitate comparability and generalisability across studies. What did the researchers do and find? This study investigated the relationship between the number and selection of conditions considered and the impact on multimorbidity prevalence. There are large differences in prevalence, a range of 4.6% to 40.5%, when different numbers and selections of conditions are considered. Nine published condition-lists were examined; including those recommended for measuring multimorbidity, previously used to measure multimorbidity prevalence, or measures of ‘comorbidity’. Highest multimorbidity prevalence was found when using Ho always + usually (a list derived from a recent Delphi consensus study), Barnett (widely used to measure multimorbidity prevalence), and Fortin (a list recommended for use in measuring multimorbidity). People who are the oldest, living in the most deprived areas, and men require fewer conditions to be considered to reach close to multimorbidity prevalence when considering all 80 conditions (the ceiling effect, where the prevalence approaches the upper limit of prevalence possible in the study). What do these findings mean? All conditions were counted in the same way (the presence of the condition ever recorded) to improve comparability, however in previous studies conditions were counted according to varying rules, highlighting that further variability in prevalence estimates across studies will happen because of variation in how each condition is measured. There is a need for standardisation when measuring multimorbidity prevalence so that results across studies are comparable and population subgroups are accurately represented. To address this, researchers can consider using the Ho always + usually, Barnett, or Fortin condition lists.