Impacts of the Primary School Free Breakfast Initiative on socio-economic inequalities in breakfast consumption among 9–11-year-old schoolchildren in Wales

Objectives - Universal interventions may widen or narrow inequalities if disproportionately effective among higher or lower socio-economic groups. The present paper examines impacts of the Primary School Free Breakfast Initiative in Wales on inequalities in children's dietary behaviours and cognitive functioning.<p></p> Design Cluster - randomised controlled trial. Responses were linked to free school meal (FSM) entitlement via the Secure Anonymised Information Linkage databank. Impacts on inequalities were evaluated using weighted school-level regression models with interaction terms for intervention × whole-school percentage FSM entitlement and intervention × aggregated individual FSM entitlement. Individual-level regression models included interaction terms for intervention × individual FSM entitlement.<p></p> Setting - Fifty-five intervention and fifty-six wait-list control primary schools.<p></p> Subjects - Approximately 4500 children completed measures of dietary behaviours and cognitive tests at baseline and 12-month follow-up.<p></p> Results School-level models indicated that children in intervention schools ate a greater number of healthy items for breakfast than children in control schools (b = 0·25; 95 % CI 0·07, 0·44), with larger increases observed in more deprived schools (interaction term b = 1·76; 95 % CI 0·36, 3·16). An interaction between intervention and household-level deprivation was not significant. Despite no main effects on breakfast skipping, a significant interaction was observed, indicating declines in breakfast skipping in more deprived schools (interaction term b = −0·07; 95 % CI −0·15, −0·00) and households (OR = 0·67; 95 % CI 0·46, 0·98). No significant influence on inequality was observed for the remaining outcomes.<p></p> Conclusions - Universal breakfast provision may reduce socio-economic inequalities in consumption of healthy breakfast items and breakfast skipping. There was no evidence of intervention-generated inequalities in any outcomes

Identifying COPD in routinely collected electronic health records: a systematic scoping review

Although routinely collected electronic health records (EHRs) are widely used to examine outcomes related to COPD, consensus regarding the identification of cases from electronic healthcare databases is lacking. We systematically examine and summarise approaches from the recent literature. MEDLINE via EBSCOhost was searched for COPD-related studies using EHRs published from January 1, 2018 to November 30, 2019. Data were extracted relating to the case definition of COPD and determination of COPD severity and phenotypes. From 185 eligible studies, we found widespread variation in the definitions used to identify people with COPD in terms of code sets used (with 20 different code sets in use based on the ICD-10 classification alone) and requirement of additional criteria (relating to age (n=139), medication (n=31), multiplicity of events (n=21), spirometry (n=19) and smoking status (n=9)). Only seven studies used a case definition which had been validated against a reference standard in the same dataset. Various proxies of disease severity were used since spirometry results and patient-reported outcomes were not often available. To enable the research community to draw reliable insights from EHRs and aid comparability between studies, clear reporting and greater consistency of the definitions used to identify COPD and related outcome measures is key

Dementias Platform UK (DPUK) Data Portal - supporting multi-modal data analysis, data linkage and real-world outcomes

DPUK relaunched the Data Portal in November 2017 to present openly available information on the data availability and technical capability of the Data Portal, which supports multi-modal research studies with various objectives from disease model validation to observation investigation. DPUK not only brings clinical data together from cohorts, but is now supporting multi-modal studies in genetics and imaging, as well as linkage opportunities to routine data using world-leading technical solutions to data sharing. The capacity, adaptability and sophistication of the UK Secure eResearch Platform which the Portal is housed on, allows for unprecedented levels of centralised access to rich cohort and routine data, which is consequentially leading to international collaboration and development ambition within epidemiology, bioinformatics, research methodology and technical research solutions. As of March 2018, DPUK is supporting 50 cohorts, 41 from the UK and 9 from across the rest of the world, alongside furthering links and access to routine data held in the UK and across the world. 20 research studies are underway, and the DPUK mission to enhance data science within dementia research is leading the conversation for developing a community of excellence in this field and across other research genres

Modelling Long Term Disability following Injury: Comparison of Three Approaches for Handling Multiple Injuries

BACKGROUND: Injury is a leading cause of the global burden of disease (GBD). Estimates of non-fatal injury burden have been limited by a paucity of empirical outcomes data. This study aimed to (i) establish the 12-month disability associated with each GBD 2010 injury health state, and (ii) compare approaches to modelling the impact of multiple injury health states on disability as measured by the Glasgow Outcome Scale - Extended (GOS-E). METHODS: 12-month functional outcomes for 11,337 survivors to hospital discharge were drawn from the Victorian State Trauma Registry and the Victorian Orthopaedic Trauma Outcomes Registry. ICD-10 diagnosis codes were mapped to the GBD 2010 injury health states. Cases with a GOS-E score >6 were defined as "recovered." A split dataset approach was used. Cases were randomly assigned to development or test datasets. Probability of recovery for each health state was calculated using the development dataset. Three logistic regression models were evaluated: a) additive, multivariable; b) "worst injury;" and c) multiplicative. Models were adjusted for age and comorbidity and investigated for discrimination and calibration. FINDINGS: A single injury health state was recorded for 46% of cases (1-16 health states per case). The additive (C-statistic 0.70, 95% CI: 0.69, 0.71) and "worst injury" (C-statistic 0.70; 95% CI: 0.68, 0.71) models demonstrated higher discrimination than the multiplicative (C-statistic 0.68; 95% CI: 0.67, 0.70) model. The additive and "worst injury" models demonstrated acceptable calibration. CONCLUSIONS: The majority of patients survived with persisting disability at 12-months, highlighting the importance of improving estimates of non-fatal injury burden. Additive and "worst" injury models performed similarly. GBD 2010 injury states were moderately predictive of recovery 1-year post-injury. Further evaluation using additional measures of health status and functioning and comparison with the GBD 2010 disability weights will be needed to optimise injury states for future GBD studies

Frailty assessed by administrative tools and mortality in patients with pneumonia admitted to the hospital and ICU in Wales

Abstract The ideal method of identifying frailty is uncertain, and data on long-term outcomes is relatively limited. We examined frailty indices derived from population-scale linked data on Intensive Care Unit (ICU) and hospitalised non-ICU patients with pneumonia to elucidate the influence of frailty on mortality. Longitudinal cohort study between 2010–2018 using population-scale anonymised data linkage of healthcare records for adults admitted to hospital with pneumonia in Wales. Primary outcome was in-patient mortality. Odds Ratios (ORs [95% confidence interval]) for age, hospital frailty risk score (HFRS), electronic frailty index (eFI), Charlson comorbidity index (CCI), and social deprivation index were estimated using multivariate logistic regression models. The area under the receiver operating characteristic curve (AUC) was estimated to determine the best fitting models. Of the 107,188 patients, mean (SD) age was 72.6 (16.6) years, 50% were men. The models adjusted for the two frailty indices and the comorbidity index had an increased odds of in-patient mortality for individuals with an ICU admission (ORs for ICU admission in the eFI model 2.67 [2.55, 2.79], HFRS model 2.30 [2.20, 2.41], CCI model 2.62 [2.51, 2.75]). Models indicated advancing age, increased frailty and comorbidity were also associated with an increased odds of in-patient mortality (eFI, baseline fit, ORs: mild 1.09 [1.04, 1.13], moderate 1.13 [1.08, 1.18], severe 1.17 [1.10, 1.23]. HFRS, baseline low, ORs: intermediate 2.65 [2.55, 2.75], high 3.31 [3.17, 3.45]). CCI, baseline  10 2.50 [2.41, 2.60]). For predicting inpatient deaths, the CCI and HFRS based models were similar, however for longer term outcomes the CCI based model was superior. Frailty and comorbidity are significant risk factors for patients admitted to hospital with pneumonia. Frailty and comorbidity scores based on administrative data have only moderate ability to predict outcome

COVID-19 Infection Risk amongst 14,104 Vaccinated Care Home Residents: A national observational longitudinal cohort study in Wales, United Kingdom, December 2020 to March 2021

Backgroundvaccinations for COVID-19 have been prioritised for older people living in care homes. However, vaccination trials included limited numbers of older people.Aimwe aimed to study infection rates of SARS-CoV-2 for older care home residents following vaccination and identify factors associated with increased risk of infection.Study Design and Settingwe conducted an observational data-linkage study including 14,104 vaccinated older care home residents in Wales (UK) using anonymised electronic health records and administrative data.Methodswe used Cox proportional hazards models to estimate hazard ratios (HRs) for the risk of testing positive for SARS-CoV-2 infection following vaccination, after landmark times of either 7 or 21 days post-vaccination. We adjusted HRs for age, sex, frailty, prior SARS-CoV-2 infections and vaccination type.Resultswe observed a small proportion of care home residents with positive polymerase chain reaction (tests following vaccination 1.05% (N = 148), with 90% of infections occurring within 28 days. For the 7-day landmark analysis we found a reduced risk of SARS-CoV-2 infection for vaccinated individuals who had a previous infection; HR (95% confidence interval) 0.54 (0.30, 0.95). For the 21-day landmark analysis, we observed high HRs for individuals with low and intermediate frailty compared with those without; 4.59 (1.23, 17.12) and 4.85 (1.68, 14.04), respectively.Conclusionsincreased risk of infection after 21 days was associated with frailty. We found most infections occurred within 28 days of vaccination, suggesting extra precautions to reduce transmission risk should be taken in this time frame

Age, sex, and socioeconomic differences in multimorbidity measured in four ways:UK primary care cross-sectional analysis

Background: Multimorbidity poses major challenges to healthcare systems worldwide. Definitions with cut-offs in excess of ≥2 long-term conditions (LTCs) might better capture populations with complexity but are not standardised. Aim: To examine variation in prevalence using different definitions of multimorbidity. Design and setting: Cross-sectional study of 1 168 620 people in England. Method: Comparison of multimorbidity (MM) prevalence using four definitions: MM2+ (≥2 LTCs), MM3+ (≥3 LTCs), MM3+ from 3+ (≥3 LTCs from ≥3 International Classification of Diseases, 10th revision chapters), and mental–physical MM (≥2 LTCs where ≥1 mental health LTC and ≥1 physical health LTC are recorded). Logistic regression was used to examine patient characteristics associated with multimorbidity under all four definitions. Results: MM2+ was most common (40.4%) followed by MM3+ (27.5%), MM3+ from 3+ (22.6%), and mental–physical MM (18.9%). MM2+, MM3+, and MM3+ from 3+ were strongly associated with oldest age (adjusted odds ratio [aOR] 58.09, 95% confidence interval [CI] = 56.13 to 60.14; aOR 77.69, 95% CI = 75.33 to 80.12; and aOR 102.06, 95% CI = 98.61 to 105.65; respectively), but mental–physical MM was much less strongly associated (aOR 4.32, 95% CI = 4.21 to 4.43). People in the most deprived decile had equivalent rates of multimorbidity at a younger age than those in the least deprived decile. This was most marked in mental–physical MM at 40–45 years younger, followed by MM2+ at 15–20 years younger, and MM3+ and MM3+ from 3+ at 10–15 years younger. Females had higher prevalence of multimorbidity under all definitions, which was most marked for mental–physical MM. Conclusion: Estimated prevalence of multimorbidity depends on the definition used, and associations with age, sex, and socioeconomic position vary between definitions. Applicable multimorbidity research requires consistency of definitions across studies

Drug prescriptions and dementia incidence: a medication-wide association study of 17000 dementia cases among half a million participants

Previous studies have suggested that some medications may influence dementia risk. We conducted a hypothesis-generating medication-wide association study to investigate systematically the association between all prescription medications and incident dementia. We used a population-based cohort within the Secure Anonymised Information Linkage (SAIL) databank, comprising routinely-collected primary care, hospital admissions and mortality data from Wales, UK. We included all participants born after 1910 and registered with a SAIL general practice at ≤60 years old. Follow-up was from each participant's 60th birthday to the earliest of dementia diagnosis, deregistration from a SAIL general practice, death or the end of 2018. We considered participants exposed to a medication if they received ≥1 prescription for any of 744 medications before or during follow-up. We adjusted for sex, smoking and socioeconomic status. The outcome was any all-cause dementia code in primary care, hospital or mortality data during follow-up. We used Cox regression to calculate hazard ratios and Bonferroni-corrected p values. Of 551 344 participants, 16 998 (3%) developed dementia (median follow-up was 17 years for people who developed dementia, 10 years for those without dementia). Of 744 medications, 221 (30%) were associated with dementia. Of these, 217 (98%) were associated with increased dementia incidence, many clustering around certain indications. Four medications (all vaccines) were associated with a lower dementia incidence. Almost a third of medications were associated with dementia. The clustering of many drugs around certain indications may provide insights into early manifestations of dementia. We encourage further investigation of hypotheses generated by these results. [Abstract copyright: © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.

Defining Acute Kidney Injury Episodes

Background Acute Kidney Injury (AKI) is a common, serious condition effecting up to 20% of all hospital admissions in the UK. AKI has an agreed definition for its recognition, however there is no consensus for the duration of an AKI episode. Main Aim To describe four different potential definitions of an AKI episode. Method We identified AKI using an SQL (Structured Query Language) based algorithm (an implementation of the NHS England eAlert algorithm) applied to serum creatinine (SCr) results from a South Wales population of ~518,000 people, held in the Secure Anonymised Information Linkage (SAIL) Databank. Using a person’s index AKI case, we applied four different rules to define an episode of AKI. These definitions are: ALERTS - until they no longer trigger an AKI eAlert, 90 DAYS - until 90 days post first AKI test and <1.2/<1.5 until the SCr recovers to <1.2 or 1.5 times their baseline creatinine. Results There were 1,832,122 SCr tests in 340,908 people between 2011-2013, of which 93,843 were alerts (5.12%). This fell to 81,948 alerts in 21,979 patients when dialysis and transplant patients were excluded. Of these patients with AKI 7,792 (35.5%) were dead at 1 year after their first episode. There were 31,505, 33,759, 26,657, 34,904 episodes in patients by <1.2, <1.5, 90 Days and ALERTS definitions respectively. Conclusion AKI episodes can be created in SAIL using SQL, and by adjusting the definition we see a variation in the number of episodes that a patient experiences. Once described, this cohort can be used to define a gold standard for AKI in future analysis

Short-term health and social impacts of energy-efficiency investments in low-income communities: a controlled field study

Background During 2012–15, £45 million was invested to improve the energy-efficiency of 4800 houses in low-income areas across Wales. Houses received measures such as external wall insulation, new windows and doors, upgrades to the heating system, and connection to the gas network. This study aimed to establish the short-term health and social impacts of these investments. Methods A quasi-experimental field study with a controlled, before and after design was conducted (364 individuals in improved houses [intervention], 418 in houses with no improvements [control]). Any adult living in 24 selected intervention areas and matched control areas (n=23) was eligible for inclusion. Self-completed questionnaires, administered via a drop-off-and-collect method, were collected in the winter months (December to February) before and after installation of the energy efficiency measures. Health outcomes were mental health composite scale (MCS) and physical health composite scale (PCS) scores of the SF-12v2, SF-6D utility scores derived from the SF-12v2, self-reported respiratory symptoms, and subjective wellbeing. Social outcomes were financial difficulties and stress, food security, thermal comfort, housing conditions, and social isolation. The study used measures validated in previous research. Linear, ordered multinomial, and logistic multilevel models were constructed with measurement occasions nested within individuals. Findings After controlling for sex, age, housing benefit, household income, and smoking status, we found that investments were not associated with improvements in MCS (B=0·00, 95% CI −1·60 to 1·60) or PCS (0·98, −0·34 to 2·28) scores, SF-6D utilities (−0·01, −0·04 to 0·02), or self-reported respiratory symptoms (−0·14, −0·54 to 0·26). However, people who received energy-efficiency measures reported improved subjective wellbeing compared with controls (B=0·38, 95% CI 0·12 to 0·65), and fewer financial difficulties (−0·15, −0·25 to −0·05); they reported higher thermal comfort (odds ratio 3·83, 95% CI 2·40 to 5·90), higher satisfaction with the improvement of their homes (3·87, 2·51 to 5·96), and less reluctance to invite friends or family to their homes (0·32, 0·13 to 0·77). Interpretation Although there is no evidence that energy-efficiency investments provide physical health benefits in the short term, they improve social and economic conditions that are conducive to better health. Longer term studies are needed to establish the health impacts of energy-efficiency investments