IntroductionThe emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.Methods and analysisTwo privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.Ethics and disseminationThe Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

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BackgroundPoor-quality housing adversely affects residents’ health but there is a paucity of high-quality evidence to support this.ObjectiveThis research investigated the health impact of bringing housing to a national quality standard.DesignA natural experiment of improvements to housing quality analysed using repeated measures of health-care utilisation and economic outcomes at an individual person level.SettingCarmarthenshire, UK.ParticipantsA total of 32,009 residents registered for a minimum of 60 days at 8558 social homes that received housing improvements between January 2005 and March 2015.InterventionsMultiple internal and external housing improvements, including wall and loft insulation, windows and doors, heating system upgrades, new kitchens and bathrooms, garden path safety improvements and electrical system upgrades (adding power sockets, and extractor fans in kitchens and bathrooms).Main outcome measuresEmergency hospital admissions for cardiorespiratory conditions and injuries. Primary health-care utilisation for respiratory and common mental health disorders, emergency department injury attendances and health-care utilisation costs.Data sourcesCarmarthenshire County Council home address and intervention records were anonymously linked within the Secure Anonymous Information Linkage databank to demographic information from the Welsh Demographic Service data set; hospital admission data from the Patient Episode Dataset for Wales; primary care contacts and prescribed medications from general practice data; emergency department attendances from the Emergency Department Data Set; and deaths from the Office for National Statistics mortality register.MethodsThe study used a longitudinal panel design to examine changes in standard of eight housing cointervention from intervention records, and linked to individuals registered at intervention homes. Health outcomes were obtained retrospectively for each individual in a dynamic cohort and were captured for up to 123 consecutive months. An additional local authority region could not be utilised as a comparator owing to different reporting pressures resulting in the recording of a different intervention. The exposure group for each cointervention was compared with an internal reference group of people living in homes that did not receive the cointervention during their tenancy. A multilevel modelling approach was used to account for repeated observations for individuals living in intervention homes. Counts of health outcomes were analysed using negative binomial regression models to determine the effect of each cointervention that reached housing quality standards during an individual’s period of tenancy, compared with those living in properties that did not. We adjusted for potential confounding factors and for background trends in the regional general population. A cost–consequences analysis was conducted as part of the health economic evaluation.ResultsResidents aged ≥ 60 years living in homes in which electrical systems were upgraded were associated with 39% fewer admissions than those living in homes in which they were not [incidence rate ratio (IRR) 0.61, 95% confidence interval (CI) 0.53 to 0.72;p < 0.01]. Reduced admissions were also associated with windows and doors (IRR 0.71, 95% CI 0.63 to 0.81;p < 0.01), wall insulation (IRR 0.75, 95% CI 0.67 to 0.84;p < 0.01) and gardens and estates (IRR 0.73, 95% CI 0.64 to 0.83;p < 0.01) for those living in homes in which these cointervention were upgraded. There were no associations of change in emergency admissions with upgrading heating (IRR 0.91, 95% CI 0.82 to 1.01;p = 0.072), loft insulation (IRR 0.98, 95% CI 0.86 to 1.11;p = 0.695), kitchens (IRR 0.98, 95% CI 0.83 to 1.17;p = 0.843) or bathrooms (IRR 0.93, 95% CI 0.81 to 1.06;p = 0.287).LimitationsThere was no randomisation, there were incomplete data on the scale of the intervention for individual households and there were no estimates for the impact of the whole programme.ConclusionsThis complex interdisciplinary study found that hospital admissions could be avoided through improving housing quality standards.Future workAt their initiation, future non-health projects should have a built-in evaluation to allow intervention exposures to be randomly allocated to residents, with the simultaneous analysis of multiple health outcomes in one statistical model.FundingThe National Institute for Health Research Public Health Research programme.

This best evidence synthesis indicates that several resident and property characteristics are associated with risk of experiencing house fire incidents, injuries or death. These findings should be considered by the Fire and Rescue Services and others with a role in fire prevention. Future research should adopt robust, standardised study designs to permit meta-analyses and enable stronger conclusions to be drawn.

The incidence of thyroid cancer has increased in Wales, predominantly due to an increase in papillary cancers. The current geographical distribution of cases does not support a radiation effect in the region. Survival has remained poor for patients over the age of 65 years and those with anaplastic carcinoma.

IntroductionMultimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence.We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity.Methods and analysisThe WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation.Ethics and disseminationThe SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

Objective To compare the effectiveness of molnupiravir, nirmatrelvir-ritonavir, and sotrovimab with no treatment in preventing hospital admission or death in higher-risk patients infected with SARS-CoV-2 in the community. Design Retrospective cohort study of non-hospitalised adult patients with COVID-19 using the Secure Anonymised Information Linkage (SAIL) Databank. Setting A real-world cohort study was conducted within the SAIL Databank (a secure trusted research environment containing anonymised, individual, population-scale electronic health record (EHR) data) for the population of Wales, UK. Participants Adult patients with COVID-19 in the community, at higher risk of hospitalisation and death, testing positive for SARS-CoV-2 between 16th December 2021 and 22nd April 2022. Interventions Molnupiravir, nirmatrelvir-ritonavir, and sotrovimab given in the community by local health boards and the National Antiviral Service in Wales. Main outcome measures All-cause admission to hospital or death within 28 days of a positive test for SARS-CoV-2. Statistical analysis Cox proportional hazard model with treatment status (treated/untreated) as a time-dependent covariate and adjusted for age, sex, number of comorbidities, Welsh Index of Multiple Deprivation, and vaccination status. Secondary subgroup analyses were by treatment type, number of comorbidities, and before and on or after 20th February 2022, when omicron BA.1 and omicron BA.2 were the dominant subvariants in Wales. Results Between 16th December 2021 and 22nd April 2022, 7,103 higher-risk patients were eligible for inclusion in the study. Of these, 2,040 received treatment with molnupiravir (359, 17.6%), nirmatrelvir-ritonavir (602, 29.5%), or sotrovimab (1,079, 52.9%). Patients in the treatment group were younger (mean age 53 vs 57 years), had fewer comorbidities, and a higher proportion had received four or more doses of the COVID-19 vaccine (36.3% vs 17.6%). Within 28 days of a positive test, 628 (9.0%) patients were admitted to hospital or died (84 treated and 544 untreated). The primary analysis indicated a lower risk of hospitalisation or death at any point within 28 days in treated participants compared to those not receiving treatment. The adjusted hazard rate was 35% (95% CI: 18-49%) lower in treated than untreated participants. There was no indication of the superiority of one treatment over another and no evidence of a reduction in risk of hospitalisation or death within 28 days for patients with no or only one comorbidity. In patients treated with sotrovimab, the event rates before and on or after 20th February 2022 were similar (5.0% vs 4.9%) with no significant difference in the hazard ratios for sotrovimab between the time periods. Conclusions In higher-risk adult patients in the community with COVID-19, those who received treatment with molnupiravir, nirmatrelvir-ritonavir, or sotrovimab were at lower risk of hospitalisation or death than those not receiving treatment.

BackgroundThere is no evidence to date on whether an intervention alerting people to high levels of pollution is effective in reducing health service utilisation. We evaluated alert accuracy and the effect of a targeted personal air pollution alert system, airAware, on emergency hospital admissions, emergency department attendances, general practitioner contacts and prescribed medications.MethodsQuasi-experimental study describing accuracy of alerts compared with pollution triggers; and comparing relative changes in healthcare utilisation in the intervention group to those who did not sign-up. Participants were people diagnosed with asthma, chronic obstructive pulmonary disease (COPD) or coronary heart disease, resident in an industrial area of south Wales and registered patients at 1 of 4 general practices. Longitudinal anonymised record linked data were modelled for participants and non-participants, adjusting for differences between groups.ResultsDuring the 2-year intervention period alerts were correctly issued on 208 of 248 occasions; sensitivity was 83.9% (95% CI 78.8% to 87.9%) and specificity 99.5% (95% CI 99.3% to 99.6%). The intervention was associated with a 4-fold increase in admissions for respiratory conditions (incidence rate ratio (IRR) 3.97; 95% CI 1.59 to 9.93) and a near doubling of emergency department attendance (IRR=1.89; 95% CI 1.34 to 2.68).ConclusionsThe intervention was associated with increased emergency admissions for respiratory conditions. While findings may be context specific, evidence from this evaluation questions the benefits of implementing near real-time personal pollution alert systems for high-risk individuals.