Introduction: COVID-19 has had a huge impact on society and healthcare and it has been suggested that people with periodontal disease are at risk of having worse outcomes from the disease. The aim of this study was to quantify the impact of periodontal disease on hospital admission and mortality during the COVID-19 pandemic.Materials and Methods: The study extracted UK Biobank participants who had taken a COVID-19 test between March and June 2020 (n = 13,253), of which 1,616 were COVID-19 positive (12%) and 11,637 were COVID-19 negative (88%). Self-reported oral health indicators of painful or bleeding gums and loose teeth were used as surrogates for periodontal disease, participants who did not report any of the aforementioned indicators were used as controls. Multivariable logistic regressions were used to obtain crude and adjusted odds ratios of COVID-19 infection, subsequent hospital admission and mortality adjusted for demographics, BMI, biomarkers, lifestyle and co-morbidities.Results: Painful gums, bleeding gums and loose teeth were reported in 2.7, 11.2 and 3.3% of participants with COVID-19 infection, respectively. Risk of COVID-19 infection in participants with painful or bleeding gums and loose teeth compared to controls was not increased (odds ratio [OR]: 1.10, 95% CI: 0.72–1.69; OR: 1.15, 95% CI: 0.84–1.59). COVID-19 positive participants with painful or bleeding gums had a higher risk of mortality (OR: 1.71, 95% CI: 1.05–2.72) but not hospital admission (OR: 0.90, 95% CI: 0.59–1.37). Participants with loose teeth did not show higher risk of hospital admission or mortality compared to the control group (OR = 1.55, 95% CI: 0.87–2.77; OR: 1.85; 95% CI: 0.92–2.72).Conclusion: There was insufficient evidence to link periodontal disease with an increased risk of COVID-19 infection. However, amongst the COVID-19 positive, there was significantly higher mortality for participants with periodontal disease. Utilization of linked dental and hospital patient records would improve the understanding of the impact of periodontal disease on COVID-19 related outcomes.

Objectives Cardiovascular disease (CVD) is a major cause of mortality; periodontal disease (PD) affects up to 50% of the world's population. Observational evidence has demonstrated association between CVD and PD. Absent from the literature is a systematic review and meta‐analysis of longitudinal cohort studies quantifying CVD risk in PD populations compared to non‐PD populations. To examine the risk of incident CVD in people with PD in randomised controlled trials and longitudinal cohort studies. Material and Methods We searched Medline, EMBASE and Cochrane databases up to 9th Oct 2019 using keywords and MeSH headings using the following concepts: PD, CVD, longitudinal and RCT study design. CVD outcomes included but were not restricted to any CVD, myocardial infarction, coronary heart disease (CHD) and stroke. Diagnosis method and severity of PD were measured either clinically or by self‐report. Studies comparing incident CVD in PD and non‐PD populations were included. Meta‐analysis and meta‐regression was performed to determine risk of CVD in PD populations and examine the effects of PD diagnosis method, PD severity, gender and study region. Results Thirty‐two longitudinal cohort studies were included after full text screening; 30 were eligible for meta‐analysis. The risk of CVD was significantly higher in PD compared to non‐PD (relative risk [RR]: 1.20, 95% CI: 1.14–1.26). CVD risk did not differ between clinical or self‐reported PD diagnosis (RR = 0.97, 95% CI: 0.87–1.07,). CVD risk was higher in men (RR: 1.16, 95% CI: 1.08–1.25) and severe PD (RR: 1.25, 95% CI: 1.15–1.35). Among all types of CVD, the risk of stroke was highest (RR = 1.24; 95% CI:1.12–1.38), the risk of CHD was also increased (RR = 1.14; 95% CI:1.08–1.21). Conclusion This study demonstrated modest but consistently increased risk of CVD in PD populations. Higher CVD risk in men and people with severe PD suggests population‐targeted interventions could be beneficial.

This study aims to examine the impact of periodontal disease in obesity on COVID-19 infection and associated outcomes. This retrospective longitudinal study included 58,897 UK Biobank participants tested for COVID-19 between March 2020 and February 2021. Self-reported oral health indicators (bleeding gums, painful gums, and loose teeth) were used as surrogates for periodontal disease. Body fat levels were quantified by body mass index (BMI) and categorized as normal weight (18.5 to 24.9 kg/m2), overweight (25 to 29.9 kg/m2), and obese (≥30 kg/m2). Multivariable logistic regression and Cox proportional hazard models were used to quantify risk of COVID-19 infection, hospital admission, and mortality, adjusted for participants’ demographics and covariates. Of 58,897 participants, 14,466 (24.6%) tested positive for COVID-19 infection. COVID-19 infection was higher for participants who were overweight (odds ratio, 1.18; 95% CI, 1.12 to 1.24) and obese (odds ratio, 1.33; 95% CI, 1.26 to 1.41) as compared with those of normal weight, but infection was not affected by periodontal disease. The hospital admission rate was 57% higher (hazard ratio, 1.57; 95% CI, 1.25 to 1.97) in the obese group with periodontal disease than without periodontal disease, and admission rates increased with BMI category (normal weight, 4.4%; overweight, 6.8%; obese, 10.1%). Mortality rates also increased with BMI category (normal weight, 1.9%; overweight, 3.17%; obese, 4.5%). In addition, for participants with obesity, the mortality rate was much higher (hazard ratio, 3.11; 95% CI, 1.91 to 5.06) in participants with periodontal disease than those without. Obesity is associated with higher hospitalization and mortality rates, and periodontal disease may exacerbate this impact. The results could inform health providers, policy makers, and the general public of the importance to maintain good oral health through seamless provision of dental services and public oral health prevention initiatives.

Aim: Periodontitis is a multifactorial condition linked to increased risk of systemic diseases. This study aimed to identify disease trajectories of people with periodontitis using the process mining technique as a heuristic approach.Materials and methods: A total of 188,863 participants from the UK Biobank cohort were included. Self-reported oral health indicators (bleeding gums, painful gums, loose teeth) were surrogates for periodontitis at baseline. Systemic disease diagnoses and dates formed the process mining event log. Relative risk (RR) of systemic diseases, disease trajectories, and Cox proportional hazard ratio models for mortality were compared to age-and sex-matched controls who did not report a history of periodontitis.Results: Participants with loose teeth had shorter median time to most systemic diseases, and crude RR was increased for several diseases including cardiovascular dis-

Purpose Case-finding for common mental disorders (CMD) in routine data unobtrusively identifies patients for mental health research. There is absence of a review of studies examining CMD-case-finding accuracy in routine primary care data. CMDcase definitions include diagnostic/prescription codes, signs/symptoms, and free text within electronic health records. This systematic review assesses evidence for case-finding accuracy of CMD-case definitions compared to reference standards. Methods PRISMA-DTA checklist guided review. Eligibility criteria were outlined prior to study search; studies compared CMD-case definitions in routine primary care data to diagnostic interviews, screening instruments, or clinician judgement. Studies were quality assessed using QUADAS-2. Results Fourteen studies were included, and most were at high risk of bias. Nine studies examined depressive disorders and seven utilised diagnostic interviews as reference standards. Receiver operating characteristic (ROC) planes illustrated overall variable case-finding accuracy across case definitions, quantified by Youden's index. Forest plots demonstrated most case definitions provide high specificity. Conclusion Case definitions effectively identify cases in a population with good accuracy and few false positives. For 100 anxiety cases, identified using diagnostic codes, between 12 and 20 will be false positives; 0-47 cases will be missed. Sensitivity is more variable and specificity is higher in depressive cases; for 100 cases identified using diagnostic codes, between 0 and 87 will be false positives; 4-18 cases will be missed. Incorporating context to case definitions may improve overall case-finding accuracy. Further research is required for meta-analysis and robust conclusions.