Syndromic case definitions for Lower Respiratory Tract Infection (LRTI) are less sensitive in older age: an analysis of symptoms among hospitalised adults

Syndromic case definitions for Lower Respiratory Tract Infection (LRTI) are less sensitive in older age: an analysis of symptoms among hospitalised adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Syndromic case definitions for Lower Respiratory Tract Infection (LRTI) are less sensitive in older age: an analysis of symptoms among hospitalised adults Rachel Kwiatkowska, Anastasia Chatzilena, Jade King, Madeleine Clout, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3933825/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Lower Respiratory Tract Infections (LRTI) pose a serious threat to older adults but may be underdiagnosed due to atypical presentations. Here we assess LRTI symptom profiles and syndromic (symptom-based) case ascertainment in older (≥65y) as compared to younger adults (<65y). Methods We included adults (≥18y) with confirmed LRTI admitted to two acute care Trusts in Bristol, UK from 1st August 2020- 31st July 2022. Logistic regression was used to assess whether age ≥65y reduced the probability of meeting syndromic LRTI case definitions, using patients’ symptoms at admission. We also calculated relative symptom frequencies (log-odds ratios) and evaluated how symptoms were clustered across different age groups. Results Of 17,620 clinically confirmed LRTI cases, 8,487 (48.1%) had symptoms meeting the case definition. Compared to those not meeting the definition these cases were younger, had less severe illness and were less likely to have received a SARS-CoV-2 vaccination or to have active SARS-CoV-2 infection. Prevalence of dementia/cognitive impairment and levels of comorbidity were lower in this group. After controlling for sex, dementia and comorbidities, age ≥65y significantly reduced the probability of meeting the case definition (aOR=0.67, 95% CI:0.63-0.71). Cases aged ≥65y were less likely to present with fever and LRTI-specific symptoms (e.g., pleurisy, sputum) than younger cases, and those aged ≥85y were characterised by lack of cough but frequent confusion and falls. Conclusions LRTI symptom profiles changed considerably with age in this hospitalised cohort. Standard screening protocols may fail to detect older and frailer cases of LRTI based on their symptoms. Respiratory tract infections pneumonia public health surveillance missed diagnosis age factors. Figures Figure 1 Figure 2 INTRODUCTION Lower Respiratory Tract Infections (LRTI) are a leading cause of morbidity and mortality and accounted for more than 2 million deaths per year before the SARS-CoV-2 virus emerged.( 1 ) 1 The SARS-CoV-2 pandemic has highlighted the vulnerability of older people to LRTI, with more than 2.8 million excess deaths estimated among people aged > 70 years (y) in 2020.( 2 ) Although older adults suffer disproportionately high rates of LRTI,( 3 ) diagnoses may be missed as patient frailty deters clinicians from taking samples and diagnostic tests perform poorly in this group. ( 4 , 5 ) This places greater importance on syndromic or symptom-based diagnoses to detect and treat cases promptly, avoid secondary transmission, and inform public health interventions to reduce disease burden.( 6 ) To identify LRTI, standardised case definitions are used which typically include fever, cough, and other signs of respiratory disease such as increased/abnormal sputum production. However, these symptoms may not manifest in older age.( 5 , 7 ) Age can directly influence the way LRTI presents as immunosenescence results in a less robust immune response,( 8 ) and as lung function declines.( 3 ) In addition, chronic medical conditions accumulate through life and can mask the clinical features of infection,( 5 ) while dementia and cognitive impairment are increasingly common in older adults and may prevent people from articulating their symptoms.( 9 ) Older adults account for a growing proportion of the global population therefore detecting LRTI in this vulnerable group is a public health priority.( 9 , 10 ) We investigated whether syndromic case ascertainment was different in older adults compared to younger adults, by analysing LRTI symptom profiles of cases recruited to an ongoing prospective cohort study in Bristol, UK. METHODS Ethics The study was approved by the Health Research Authority Research Ethics Committee East of England, Essex, reference 20/EE/0157. Informed consent was obtained from cognisant patients, and declarations for participation from consultees for individuals lacking capacity. Patients who declined consent were not included in this analysis. For individuals for whom an approach to seek consent could not be made, data were included with approval from the Clinical Advisory Group (20/CAG/0138). Study Population Adults (≥ 18y) admitted to both acute care hospitals in Bristol, UK, were recruited to the AvonCAP study (ISRCTN:17354061) if they had ≥ 2 signs/symptoms compatible with acute lower respiratory tract disease (aLRTD), or a clinical or radiological aLRTD diagnosis ( Supplementary data 1 ). The study protocol is published containing full recruitment details.( 11 ) Those with a confirmed diagnosis of lower respiratory tract infection (LRTI) who were hospitalised between 1st August 2020 and 31st July 2022 were included in this analysis. Confirmed LRTI was defined as evidence of active infection plus aLRTD, or a clinician diagnosis of LRTI, or a positive laboratory or radiological test for respiratory infection ( Supplementary data 1 ). LRTI were classed as pneumonia if there were confirmed radiological changes compatible with infection, or when the treating clinician diagnosed pneumonia.( 12 ) SARS-CoV-2 infection was defined as lower respiratory tract disease and a positive test result for SARS-CoV-2 on/during hospitalisation or within 7-days prior to hospital admission, using the established UK Health Security Agency (UKHSA) diagnostic assay deployed at the time. Data Demographic and clinical data were systematically collected from patient records using REDCap.( 13 ) They included a Rockwood clinical frailty score (a score > 4 indicates frailty), ranging from people who need help with higher order instrumental activities of daily living (IADL) to those who are completely dependent.( 14 ) Comorbidity was assessed using the Charlson Comorbidity Index (CCI)( 15 ) excluding scores assigned for a diagnosis of dementia (which were recorded separately), with CCI scores categorised into none (CCI = 0), mild ( 1 – 2 ), moderate ( 3 – 4 ) or severe (> 4). Disease severity was measured using the CRB-65 score( 16 ) on admission, with a point assigned for each of: acute confusion (Abbreviated Mental Test Score ≤ 7); raised respiratory rate (≥ 30) and low blood pressure (systolic < 90mmHg or diastolic ≤ 60mmHg). As no points were assigned for age ≥ 65y since this was our outcome of interest, we term this the CRB score. The results of standard-of-care laboratory (virological and/or bacteriological) tests, chest radiology and clinical findings were also recorded. A respiratory physician reviewed all patient records, including clinician notes and investigation results, to validate the final clinical diagnosis according to pre-specified diagnostic criteria ( Supplementary data 1 ) . A syndromic case definition for suspected LRTI was constructed using signs and symptoms publicised to both the public and clinicians by the National Health Service (NHS) and British Medical Journal (BMJ), respectively ( Supplementary data 2 ).( 17 , 18 ) To meet this case definition, patients had to present with cough plus fever (reported fever/chills or temperature > 38.0°C or < 35°C), or with at least three of the following: cough, fever, breathlessness, wheeze, pleurisy (chest pain on breathing), abnormal sputum production, myalgia, headache, or general deterioration (weakness/fatigue/anorexia). Study objectives The primary objective was to determine whether older adults hospitalised with LRTI were less likely to meet the syndromic LRTI case definition when compared to younger adults. Secondary objectives were: a) describe the cohort of adults with LRTI by factors that influence clinical presentation (age, sex, levels of comorbidity/frailty, and diagnosis of dementia or cognitive impairment), and b) assess which symptom profiles characterised older adults. Statistical analysis Categorical data were summarised as counts and percentages, continuous data as medians with interquartile (IQR) ranges. The characteristics of patient groups were compared using Fisher exact tests for dichotomous variables, two-sided Kolmogorov-Smirnov tests for continuous variables and Wilcoxon rank sum tests for score variables. Density plots were used to visualise changes in cases’ age distribution and SARS-CoV-2 positivity over time, to better understand sources of bias. To assess whether LRTI case ascertainment was lower in adults ≥ 65y compared to those < 65y we built a multivariable logistic regression model. The primary exposure was age ≥ 65y, and the outcome was meeting the syndromic LRTI case definition at presentation (Yes/No). Covariates were selected based on our understanding of relationships between age, LRTI symptom expression and associated factors ( Supplementary data 4–5 ), along with results of our descriptive analyses (Table 1 , Supplementary data 6). We then stratified the analysis by levels of comorbidity (CCI > 4 versus ≤ 4), followed by SARS-CoV-2 positivity, and tested sensitivity to changes in the case definition by assessing probability of cases presenting with any diagnostic symptom. To explore differences in symptom expression we calculated the posterior log-odds of each symptom being expressed in adults ≥ 65y versus adults < 65y, employing an empirical Bayesian approach with a multinomial model and an informative Dirichlet prior estimated from the data. To account for greater variance in estimates for rarer symptoms, log-odds were weighted according to the frequency of each symptom observed and presented as z-scores of the log odds ratio. To assess whether symptom profiles changed with age, patterns of symptom expression were shown as heatmaps with patients grouped across 10-year age bands and cells coloured according to symptom frequency within each age band, relative to all other symptoms within that band. A dendrogram grouped age bands based on a Euclidean distance matrix, showing which were most closely related in terms of symptom profile. To illustrate this further a k-means clustering algorithm was applied,( 19 ) identifying which 10y age bands were most closely related in terms of symptom profiles, and age bands were assigned to clusters such that the sum of squared distances between age bands and cluster centroid was minimised. Principal Component Analysis was then conducted on LRTI symptoms, creating linear combinations (Principal Components, [PC]), such that each PC contributed to overall variance but was uncorrelated with other PCs.( 19 ) These elicited combinations of symptoms (symptom profiles) that explained most of the differences between 10y age bands. Finally, the k-means clusters were presented graphically with age bands positioned according to their first and second PC values, such that age bands with similar symptom profiles appear close together and those that differ, further apart. All analyses were conducted using R statistical software version 4.2.1.( 20 ) Missing data were limited to CRB score and comorbidity level variables - each accounting for 0.06% of the sample. No imputation was performed and multivariable logistic regression analyses only included participants with complete data. Statistical significance was defined using a 2-sided significance level of α = 0∙05. RESULTS Overall, 21,447 adults were hospitalised with aLRTD symptoms. A total of 17,620 (82.0%) were diagnosed with LRTI including 7,310 (41.5%) with a positive SARS-CoV-2 test and 10,125 (57.5%) with pneumonia. Among confirmed LRTI cases, 8,487 (48.1%) had symptoms that met the syndromic LRTI case definition ( Supplementary data 3 ). These cases were younger, less likely to live in a care home and less likely to have received the SARS-CoV-2 vaccine or have active SARS-CoV-2 infection. They were also less likely to have severe levels of comorbidity (CCI>4), a diagnosis of dementia/cognitive impairment, or at least one CRB score indicator of severe disease on admission as compared to cases whose symptoms did not meet the syndromic LRTI case definition ( Table1 ). The age distribution of SARS-CoV-2 positive cases changed over time, with a peak emerging in late 2020 through 2021 which represented younger cases with symptoms largely meeting the syndromic LRTI case definition. There was also a peak in older cases, most of whom did not have symptoms meeting the case definition, which was consistent except in quarter 2 of 2021 ( Figure 1C ). From mid-2021 older cases began to dominate, eventually mirroring the age distribution for non-SARS-CoV-2 LRTI ( Figure 1B ). The probability of symptoms meeting the syndromic LRTI case definition was significantly lower for adults aged ≥65y as compared to adults aged <65y both on univariable analysis (OR=0.62, 95% Confidence Interval (CI) 0.58-0.66) and after adjusting for sex, dementia/ cognitive impairment, and severe levels of comorbidity (aOR=0.67, 95%CI 0.63-0.71; Table 2 ). The presence of dementia/cognitive impairment also significantly reduced the probability of meeting the syndromic case definition (aOR=0.60, 95%CI 0.54-0.66). Stratified analysis yielded very similar results for cases with CCI≤4, whereas among cases with severe levels of comorbidity (CCI>4) there was no evidence of age or dementia influencing the probability of meeting the syndromic case definition ( Supplementary data 7 ). When we stratified by SARS-CoV-2 positivity and added SARS-CoV-2 vaccination status as a covariate (see DAG in Supplementary Data 4 ), having had ≥1 dose of vaccine lowered the probability of meeting the case definition for SARS-CoV-2 positive cases (aOR=0.45, 95%CI 0.41-0.50) but increased it for SARS-CoV-2 negative cases (aOR=1.41, 95%CI 1.29-1.53). Age ≥65y reduced the probability of meeting the case definition with an attenuated effect in SARS-CoV-2 LRTI as compared to other LRTI (aOR=0.68, 95%CI 0.61-0.75 versus aOR=0.57, 95%CI 0.52-0.62); dementia/cognitive impairment had the same effect in both groups. In contrast to the full LRTI cohort, among SARS-CoV-2 positive cases male sex significantly increased the probability of meeting the syndromic case definition (aOR=1.29, 95%CI 1.17-1.43), and severe levels of comorbidity had no significant effect ( Supplementary Data 8) . As a sensitivity analysis we assessed probability of cases expressing any one of the symptoms in the syndromic analysis and found no difference by age, sex, or comorbidity level. Only the presence of dementia/ cognitive impairment reduced the probability of any symptom being reported (aOR=0.58, 95%CI 0.51-0.67; Supplementary Data 9 ). Older adults (≥65y) were more likely to present with confusion, falls, and general deterioration and less likely to present with pleurisy, headache, cough, and sputum than younger adults (<65y) (log-odds of symptom expression, Figure 2A ). Analysis of symptom profiles by 10y age bands showed that cough and breathlessness were the most frequent presenting symptoms across the age spectrum, except for cases ≥85y who were less likely to cough and more likely to experience confusion and falls ( Figure 2B ). The heatmap revealed two distinct clusters separating cases above and below 55y, with fever and pleurisy more prominent below 55y. This clustering was also apparent when assessing 10y age bands by k-means clustering ( Figure 2C ), with the youngest (18-24y) and oldest (≥85y) age bands lying furthest from all others. Table 1: Characteristics of LRTI cases by whether presenting symptoms meet the case definition. Characteristic Not meeting LRTI case definition* N = 9,133 Meeting LRTI case definition* N = 8,487 p-value** Age (yrs) at admission 75 (58, 85) 68 (52, 80) <0.001 Age group <0.001 18-24 189 (2.1%) 254 (3.0%) 25-34 506 (5.5%) 492 (5.8%) 35-44 549 (6.0%) 691 (8.1%) 45-54 681 (7.5%) 958 (11%) 55-64 1,051 (12%) 1,327 (16%) 65-74 1,486 (16%) 1,641 (19%) 75-84 2,301 (25%) 1,851 (22%) >84 2,370 (26%) 1,273 (15%) SARS-CoV-2 test positive 4,259 (47%) 3,051 (36%) <0.001 Vaccinated against SARS-CoV-2 5,524 (65%) 4,865 (61%) <0.001 Unknown 683 508 CRB score a <0.001 0 5,853 (64%) 5,857 (69%) 1 2,731 (30%) 2,269 (27%) 2 501 (5.5%) 332 (3.9%) 3 41 (0.4%) 25 (0.3%) Unknown 7 4 Male sex 4,601 (50%) 4,353 (51%) 0.2 Care home resident 910 (10.0%) 482 (5.7%) <0.001 Dementia/ cognitive impairment 1,285 (14%) 668 (7.9%) <0.001 Clinically frail b 3,860 (55%) 2,785 (39%) <0.001 Unknown 2,145 1,329 Comorbidity level c 0.043 None 3,640 (40%) 3,449 (41%) Mild 3,707 (41%) 3,503 (41%) Moderate 1,245 (14%) 1,137 (13%) Severe 534 (5.9%) 394 (4.6%) Unknown 7 4 * Median (IQR); n (%) ** Welch Two Sample t-test; Wilcoxon rank sum test; Fisher's Exact Test for Count Data a Pneumonia severity score, 1 point assigned for each of: acute confusion, raised respiratory rate, low blood pressure. b Rockwood frailty score >4 c Charlson Comorbidity Index minus age and dementia scores. mild: 1-2, moderate: 3-4, severe: >4 Table 2: Logistic Regression - odds of symptoms meeting LRTI case definition. Unadjusted odds Adjusted odds Characteristic N OR* 95% CI* p-value OR* 95% CI* p-value Aged >=65y 17,620 0.62 0.58, 0.66 <0.001 0.67 0.63, 0.71 <0.001 Male sex 17,620 1.04 0.98, 1.10 0.2 1.05 0.99, 1.12 0.085 Dementia/ cognitive impairment 17,620 0.52 0.47, 0.58 <0.001 0.6 0.54, 0.66 4 (severe) a 17,609 0.78 0.69, 0.90 <0.001 0.86 0.75, 0.98 0.025 * OR = Odds Ratio, CI = Confidence Interval a Charlson Comorbidity Index minus age and dementia scores. DISCUSSION Less than half of adults hospitalised with LRTI presented with symptoms matching the syndromic LRTI case definition. Our results suggest that symptom profiles publicised to the UK public and clinicians characterise cases of LRTI among young, healthy adults but may fail to identify cases among older and frailer adults, particularly those with dementia or cognitive impairment who may not express any diagnostic symptoms. People with severe comorbidities (CCI > 4) are also at high risk of a missed diagnosis, regardless of age. We find that cases aged ≥ 65y, who made up almost two thirds of the cohort, were less likely to present with classical LRTI symptoms such as cough, fever and pleurisy when compared to younger cases, and that those aged ≥ 85y commonly presented with confusion and falls. This has implications for older patients whose infections may not be diagnosed and treated, as well as research and policy since standard screening protocols may underestimate disease burden and vaccine effectiveness in older age groups. The AvonCAP prospective cohort study provided clinically validated LRTI diagnoses and comprehensive symptoms data with which we could assess the performance of syndromic case definitions. Few studies have assessed the sensitivity of LRTI case definitions by age group or explored how symptom profiles evolve with age. This analysis has highlighted that presenting symptoms are not reliable predictors of LRTI in older patients, and identified symptom profiles which may help hospital clinicians to identify and treat cases of LRTI in this population. Researchers may also wish to modify LRTI diagnostic/screening tools to include the full spectrum of clinical presentations. Other studies of hospitalised adults have reported low sensitivity for LRTI case definitions in older adults;( 21 ) in a study of veterans aged > 40y with bacterial pneumonia, younger adults ( 80y, which was attributed to high rates of dementia, but contrary to our findings, cough was as prevalent in this age group as in others.( 22 ) Studies focussing on hospitalised adults aged > 80y have reported much higher prevalence of altered mental state (53–77%) and lower prevalence of cough (40–63%) in nursing home-acquired pneumonias as compared to community-acquired pneumonias (altered mental state: 12–45%, cough: 49–81%).( 3 ) This symptom profile may therefore be characteristic of frailer cases (frailty defined as a degree of dependence for daily activities of living),( 14 ) since cases aged ≥ 85y accounted for the greatest proportion (17%) of care home residents in our cohort. With respect to SARS-CoV-2 LRTI, which made up over 40% of our sample, data from the UK Coronavirus (COVID-19) Infection Survey also show that the probability of reporting fever declines from around the age of 60y.( 23 ) Although fever is considered a cardinal symptom of LRTI it is a poor predictor of infection in older adults,( 24 , 25 ) and its absence will contribute to case under-ascertainment. The bimodal age distribution of SARS-CoV-2 positive cases from the end of 2020 through to 2022 is likely to reflect the emergence of new variants Alpha and Delta while older adults were prioritised for vaccination. This resulted in a greater proportion of hospitalised cases among younger adults,( 23 , 26 ) whilst the steady peak in older aged cases represents frail individuals with a low threshold for hospital admission. The age distribution of sexes also differed for SARS-CoV-2 when compared to other forms of LRTI ( Supplementary data 10 ), with a peak in young females possibly representing pregnant women. Assuming these individuals had a lower severity threshold for admission, they would have been less likely to express symptoms and meet the LRTI case definition, which could explain why male sex was associated with meeting the case definition for SARS-CoV-2 positive cases ( Supplementary data 7 ). This analysis has limitations including shifts in case mix and symptom profiles over the study period as the result of time-varying factors which we did not account for, and which may have biased results of the regression analysis. These factors include seasonal fluctuations in respiratory illnesses, the emergence of SARS-CoV-2 variants, implementation of lockdowns and changes in hospital admission thresholds for care home residents and other vulnerable groups.( 27 , 28 ) Additionally, older adults were prioritised for SARS-CoV-2 vaccination, which reduced severity of illness and therefore symptom expression and may have enhanced the effect of older age on probability of meeting the case definition,( 29 ) although the effect remained strong when we conditioned on vaccination status in the SARS-CoV-2 stratification. Our regression model was designed to estimate the total effects of older age on symptom profile and did not allow us to disentangle the effects of age, frailty, vaccination, and severity of illness (see DAG in Supplementary Data 4 ). The SARS-CoV-2 virus was prioritised for testing above other pathogens during the study period, so other causes of LRTI may have been underestimated, nonetheless the majority of LRTI in our cohort and a previous AvonCAP cohort were SARS-CoV-2 negative.( 26 ) A limitation of the AvonCAP study is that it is restricted to hospitalised cases of LRTI. Since these are likely to have more severe illness and/ or to be frailer than cases in the community, our results are not generalisable to LRTI outside the hospital setting. Data were gathered retrospectively from hospital case notes so misclassification in clinical syndromes and diagnoses is possible, and our cohort may not be representative of all hospitalised LRTI. However, enrolment criteria were broad and case review thorough to minimise risk of false negatives. Finally, this is not an evaluation of clinical diagnostic tools; our syndromic case definitions are constructs used to assess whether older adults with LRTI are less likely to be diagnosed at presentation, based on commonly known symptoms. In conclusion, this analysis provides further evidence that older adults with LRTI present atypically, reducing the likelihood of timely diagnosis and successful intervention. Age-specific case definitions could improve case ascertainment and patient outcomes, although further investigation is needed to determine symptom profiles in non-hospitalised cases of LRTI, including those with cognitive impairment and living in long-term care. Declarations ACKNOWLEDGEMENTS Our thanks to colleagues at the University of Bristol for their support with this study, including Rachel Davies, Paul Savage, Emma Foose, Susan Christie, Mark Mummé, and Adam Taylor, and to Gibran Hemani for his advice on causal diagrams. Also, to Kevin Sweetland and Aman Kaur-Singh in the AvonCAP team. We would also like to acknowledge the research teams at North Bristol and University Hospitals of Bristol and Weston NHS Trusts for making this study possible, including Helen Lewis-White, Rebecca Smith, Rajeka Lazarus, Mark Lyttle, Kelly Turner, Jane Blazeby, Diana Benton, and David Wynick. We would also like to acknowledge Christian Theilacker and Maria Lahuerta for suggestions on the manuscript and Bradford Gessner, Jo Southern and Elizabeth Begier for assistance with setting up the AvonCAP study. We acknowledge the invaluable contributions of Alison Horne, Mai Baquedano, Stewart Robinson, David Clint, and Henry Stuart. A big thank you to all our study participants. The AvonCAP Research Group: Anna Morley, Amelia Langdon, Anabella Turner, Anya Mattocks, Bethany Osborne, Charli Grimes, Claire Mitchell, David Adegbite, Emma Bridgeman, Emma Scott, Fiona Perkins, Francesca Bayley, Gabriella Ruffino, Gabriella Valentine, Grace Tilzey, James Campling, Johanna Kellett Wright, Julia Brzezinska, Julie Cloake, Katarina Milutinovic, Kate Helliker, Katie Maughan, Kazminder Fox, Konstantina Minou, Lana Ward, Leah Fleming, Leigh Morrison, Lily Smart, Louise Wright, Lucy Grimwood, Maddalena Bellavia, Madeleine Clout, Marianne Vasquez, Maria Garcia Gonzalez, Milo Jeenes-Flanagan, Natalie Chang, Niall Grace, Nicola Manning, Oliver Griffiths, Pip Croxford, Peter Sequenza, Rajeka Lazarus, Rhian Walters, Robin Marlow, Robyn Heath, Rupert Antico, Sandi Nammuni Arachchge, Seevakumar Suppiah, Taslima Mona, Tawassal Riaz, Vicki Mackay, Zandile Maseko, Zoe Taylor, Zsolt Friedrich, Zsuzsa Szasz-Benczur. DATA SHARING To preserve the confidentiality of our participants the data from this study cannot be made publicly available. The code used for this analysis is available on GitHub: https://github.com/bristol-vaccine-centre/LRTI_symptoms . AUTHOR CONTRIBUTIONS RK, LD, AC and CH conceived the research question and developed the analysis plan. CH, AM, JK, MC, and The AvonCAP team were involved in data collection and RK and AC analysed the data. All authors contributed to interpretation of results and commented on the manuscript written by RK. AF provided oversight of the research. DECLARATIONS OF INTEREST/ CONFLICT OF INTEREST CH is Principal Investigator of the AvonCAP study, a University of Bristol sponsored study which is funded by Pfizer. JO is a Co-Investigator on the AvonCAP Study. AF is a member of the UK Dept of Health, Joint Committee on Vaccination and Immunization (JCVI) and, until December 2022, was chair of the World Health Organization European Technical Advisory Group of Experts on Immunization (ETAGE). In addition to receiving funding for this study from Pfizer as Chief Investigator, he leads another project investigating transmission of respiratory bacteria in families jointly funded by Pfizer and the Gates Foundation and is chief or principal investigator in current or recent COVID-19 vaccine trials funded by Astra-Zeneca, Valneva and Sanofi. LD, RC are also partly funded through AvonCAP. LD is a Co-Investigator of the AvonCAP study and has also received funding from Pfizer, UKRI and UKHSA for unrelated projects. RK holds an honorary contract with the UK Health Security Agency (UKHSA). The corresponding author had full access to all data in the study and final responsibility for the decision to submit for publication. 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A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis [Internet]. 1987 [cited 2023 Jun 8];40(5):373–83. Available from: https://pubmed.ncbi.nlm.nih.gov/3558716/ . Quality statement 4. : Mortality risk assessment in hospital using CURB65 score | Pneumonia in adults | Quality standards | NICE [Internet]. [cited 2023 Apr 14]. Available from: https://www.nice.org.uk/guidance/qs110/chapter/quality-statement-4-mortality-risk-assessment-in-hospital-using-curb65-score . National Health Service. Chest infection - NHS [Internet]. 2020 [cited 2023 Jun 8]. Available from: https://www.nhs.uk/conditions/chest-infection/ . British Medical Journal. Community-acquired pneumonia (non COVID-19) - Symptoms, diagnosis and treatment - Summary | BMJ Best Practice [Internet]. 2023 [cited 2023 Jun 8]. Available from: https://bestpractice.bmj.com/topics/en-gb/3000108 . Bishop C. Pattern Recognition and Machine Learning. 8th ed. New York, NY: Springer Science + Business Media, LLC; 2006. R core team; R Foundation for Statistical Computing Vienna Austria. R: A language and environment for statistical computing. [Internet]. 2021. Available from: https://www.r-project.org/ . Babcock HM, Merz LR, Dubberke ER, Fraser VJ. Case-control study of clinical features of influenza in hospitalized patients. Infect Control Hosp Epidemiol [Internet]. 2008 Oct [cited 2023 Jun 8];29(10):921–6. Available from: https://pubmed.ncbi.nlm.nih.gov/18754739/ . Harper C, Newton P. Clinical Aspects of Pneumonia in the Elderly Veteran. J Am Geriatr Soc [Internet]. 1989 Sep 1 [cited 2023 Jun 8];37(9):867–72. Available from: https://onlinelibrary.wiley.com/doi/full/ 10.1111/j.1532-5415.1989.tb02268.x . Vihta KD, Pouwels KB, Peto TEA, Pritchard E, Eyre DW, House T et al. Symptoms and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Positivity in the General Population in the United Kingdom. Clin Infect Dis [Internet]. 2022 Aug 24 [cited 2023 Apr 18];75(1):e329–37. Available from: https://academic.oup.com/cid/article/75/1/e329/6423489 . Govaert TME, Dinant GJ, Aretz K, Knottnerus JA. The predictive value of influenza symptomatology in elderly people. Fam Pract [Internet]. 1998 Feb [cited 2023 Jun 8];15(1):16–22. Available from: https://pubmed.ncbi.nlm.nih.gov/9527293/ . Norman DC. Clinical Features of Infection in Older Adults. Clin Geriatr Med [Internet]. 2016 Aug 1 [cited 2022 Jan 2];32(3):433–41. Available from: https://pubmed.ncbi.nlm.nih.gov/27394015/ . Hyams C, Challen R, Begier E, Southern J, King J, Morley A et al. Incidence of community acquired lower respiratory tract disease in Bristol, UK during the COVID-19 pandemic: A prospective cohort study. Lancet Reg Heal - Eur [Internet]. 2022 Oct 1 [cited 2023 Jun 16];21. Available from: http://www.thelancet.com/article/S2666776222001697/fulltext . Yang M, Shi L, Chen H, Wang X, Jiao J, Liu M et al. Critical policies disparity of the first and second waves of COVID-19 in the United Kingdom. Int J Equity Health [Internet]. 2022 Dec 1 [cited 2023 Jun 8];21(1):1–11. Available from: https://equityhealthj.biomedcentral.com/articles/ 10.1186/s12939-022-01723-3 . Vos LM, Bruyndonckx R, Zuithoff NPA, Little P, Oosterheert JJ, Broekhuizen BDL et al. Lower respiratory tract infection in the community: associations between viral aetiology and illness course. Clin Microbiol Infect [Internet]. 2021 Jan 1 [cited 2023 Jun 8];27(1):96. Available from: /pmc/articles/PMC7118666/ . Hyams C, Challen R, Marlow R, Nguyen J, Begier E, Southern J, et al. Severity of Omicron (B.1.1.529) and Delta (B.1.617.2) SARS-CoV-2 infection among hospitalised adults: a prospective cohort study in Bristol, United Kingdom. Lancet Reg Heal - Eur. 2023;25:100556. Additional Declarations Competing interest reported. CH is Principal Investigator of the AvonCAP study, a University of Bristol sponsored study which is funded by Pfizer. JO is a Co-Investigator on the AvonCAP Study. AF is a member of the UK Dept of Health, Joint Committee on Vaccination and Immunization (JCVI) and, until December 2022, was chair of the World Health Organization European Technical Advisory Group of Experts on Immunization (ETAGE). In addition to receiving funding for this study from Pfizer as Chief Investigator, he leads another project investigating transmission of respiratory bacteria in families jointly funded by Pfizer and the Gates Foundation and is chief or principal investigator in current or recent COVID-19 vaccine trials funded by Astra-Zeneca, Valneva and Sanofi. LD, RC are also partly funded through AvonCAP. LD is a Co-Investigator of the AvonCAP study and has also received funding from Pfizer, UKRI and UKHSA for unrelated projects. RK holds an honorary contract with the UK Health Security Agency (UKHSA). The corresponding author had full access to all data in the study and final responsibility for the decision to submit for publication. Supplementary Files LRTIascertainmentsupplementbmc.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Mar, 2024 Reviews received at journal 15 Feb, 2024 Reviewers agreed at journal 14 Feb, 2024 Reviewers invited by journal 14 Feb, 2024 Editor assigned by journal 14 Feb, 2024 Editor invited by journal 14 Feb, 2024 Submission checks completed at journal 14 Feb, 2024 First submitted to journal 06 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3933825","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271571832,"identity":"308cf639-1bc1-408a-bc15-e4995c3d5e92","order_by":0,"name":"Rachel Kwiatkowska","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Kwiatkowska","suffix":""},{"id":271571833,"identity":"e6e5aa14-89e1-40a6-9c12-0fd6dc8e46dd","order_by":1,"name":"Anastasia Chatzilena","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Anastasia","middleName":"","lastName":"Chatzilena","suffix":""},{"id":271571834,"identity":"b144608a-ddd7-41b8-938d-3236c02347d1","order_by":2,"name":"Jade King","email":"","orcid":"","institution":"UHBW NHS Trust","correspondingAuthor":false,"prefix":"","firstName":"Jade","middleName":"","lastName":"King","suffix":""},{"id":271571835,"identity":"04b08865-ad91-44db-932a-7fb9b058b43e","order_by":3,"name":"Madeleine Clout","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Madeleine","middleName":"","lastName":"Clout","suffix":""},{"id":271571836,"identity":"4067a7d0-d0f2-4f63-9e91-c6d3cc5ee610","order_by":4,"name":"Serena McGuinness","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Serena","middleName":"","lastName":"McGuinness","suffix":""},{"id":271571837,"identity":"1ae00bd7-3123-4043-8fd9-9d9e6a91875e","order_by":5,"name":"Nick Maskell","email":"","orcid":"","institution":"North Bristol NHS Trust, Southmead Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nick","middleName":"","lastName":"Maskell","suffix":""},{"id":271571838,"identity":"ad69ae08-4256-448c-919c-3144a93fdbc8","order_by":6,"name":"Jennifer Oliver","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Oliver","suffix":""},{"id":271571839,"identity":"c9177418-b6c8-4cd3-92cc-856aca6942b7","order_by":7,"name":"Robert Challen","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Challen","suffix":""},{"id":271571840,"identity":"4d78162e-0800-4278-92ad-c3d7d7b8ace9","order_by":8,"name":"Matthew Hickman","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Hickman","suffix":""},{"id":271571841,"identity":"f95d3a3f-7231-480e-bfc4-5186f13b9901","order_by":9,"name":"Adam Finn","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Finn","suffix":""},{"id":271571842,"identity":"799c9594-edd5-4078-816f-406748dc80fb","order_by":10,"name":"Catherine Hyams","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Hyams","suffix":""},{"id":271571843,"identity":"ff50fb6e-4c0c-41a7-ba8c-7d9e42f92cea","order_by":11,"name":"Leon Danon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACPgYGAxAtZ8DA2ACkLcCiYCYuwAbVYgzWcoBBgngtiRtAJJFamDd+Lqi4k75d+nDz5w8VEgz87QfYJGfg1cJWLD3jzLPcnX2JbRIHzkgwSJxJYJPcgFcLj4E0b9vh3A1nGNsYDrYBHXaDgU3yAX4txr+BWtINzjA2fwBpkSdCixnIlgSglgYJkBYDkBa8DmNmK7PmOXPYcGcPY5vEmTMSPIZnEpst8Xmfn715822eisPy5jzsjz9UVNjIyR0/fPBmDx4tDMxofB4CsTIKRsEoGAWjgBgAAF+4RcfEBRjjAAAAAElFTkSuQmCC","orcid":"","institution":"University of Bristol","correspondingAuthor":true,"prefix":"","firstName":"Leon","middleName":"","lastName":"Danon","suffix":""}],"badges":[],"createdAt":"2024-02-06 12:21:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3933825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3933825/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51077942,"identity":"63b68124-0a47-47ef-9f9e-5a165c66e622","added_by":"auto","created_at":"2024-02-13 18:52:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge distribution of LRTI cases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe age distribution of LRTI cases by SARS-CoV-2 status and whether patients presented with symptom profiles consistent with the LRTI case definition. \u003cstrong\u003e(A)\u003c/strong\u003eCounts of cases per quarter, by year of age, with dotted lines showing counts of SARS-CoV-2 LRTI and solid lines showing counts of other LRTI; \u003cstrong\u003e(B)\u003c/strong\u003e Age distribution of SARS-CoV-2 negative cases and \u003cstrong\u003e(C)\u003c/strong\u003e SARS-CoV-2 positive cases. Each row represents a quarter (Q) from Q3 2020 through to Q3 2022, except for the first and last rows which are truncated due to the time period of this analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3933825/v1/b53a1ef9fe634aba5a6a9d79.png"},{"id":51077944,"identity":"e579b5fb-047a-463a-8cd1-793cc9d23357","added_by":"auto","created_at":"2024-02-13 18:52:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123756,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge-specific symptom profiling for LRTI.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Comparison of LRTI symptom expression in older (≥65y) vs younger (\u0026lt;65y) adults is shown as a probability of each symptom occurring, with weighted log-odds ratios expressed as z-scores (number of standard deviations from the mean). \u003cstrong\u003e(B)\u003c/strong\u003eCases are grouped in 10y age bands (rows), and cells are coloured based on frequency of a symptom within each age band relative to all other symptoms within the age band. Frequencies are presented as z-scores. \u003cstrong\u003e(C) \u003c/strong\u003eAge bands are assigned to clusters based on similarity of LRTI symptom profiles and displayed along axes defined by Principal Components PC1 and PC2.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3933825/v1/3bbeffdb1bd5f3de15c91b4a.png"},{"id":51079314,"identity":"6ba3eb5a-f9d9-42d1-bac5-ff8cb1a04c94","added_by":"auto","created_at":"2024-02-13 19:00:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":704001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3933825/v1/386b050e-d80d-4a78-b38f-5eaae77e811c.pdf"},{"id":51077943,"identity":"2eef673e-1bcb-4628-8344-c17a68a790c2","added_by":"auto","created_at":"2024-02-13 18:52:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":266439,"visible":true,"origin":"","legend":"","description":"","filename":"LRTIascertainmentsupplementbmc.docx","url":"https://assets-eu.researchsquare.com/files/rs-3933825/v1/09e89004d82c3506b9e9d97c.docx"}],"financialInterests":"Competing interest reported. CH is Principal Investigator of the AvonCAP study, a University of Bristol sponsored study which is funded by Pfizer. JO is a Co-Investigator on the AvonCAP Study. AF is a member of the UK Dept of Health, Joint Committee on Vaccination and Immunization (JCVI) and, until December 2022, was chair of the World Health Organization European Technical Advisory Group of Experts on Immunization (ETAGE). In addition to receiving funding for this study from Pfizer as Chief Investigator, he leads another project investigating transmission of respiratory bacteria in families jointly funded by Pfizer and the Gates Foundation and is chief or principal investigator in current or recent COVID-19 vaccine trials funded by Astra-Zeneca, Valneva and Sanofi. LD, RC are also partly funded through AvonCAP. LD is a Co-Investigator of the AvonCAP study and has also received funding from Pfizer, UKRI and UKHSA for unrelated projects. RK holds an honorary contract with the UK Health Security Agency (UKHSA). The corresponding author had full access to all data in the study and final responsibility for the decision to submit for publication.","formattedTitle":"Syndromic case definitions for Lower Respiratory Tract Infection (LRTI) are less sensitive in older age: an analysis of symptoms among hospitalised adults","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLower Respiratory Tract Infections (LRTI) are a leading cause of morbidity and mortality and accounted for more than 2\u0026nbsp;million deaths per year before the SARS-CoV-2 virus emerged.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003csup\u003e1\u003c/sup\u003eThe SARS-CoV-2 pandemic has highlighted the vulnerability of older people to LRTI, with more than 2.8\u0026nbsp;million excess deaths estimated among people aged\u0026thinsp;\u0026gt;\u0026thinsp;70 years (y) in 2020.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Although older adults suffer disproportionately high rates of LRTI,(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) diagnoses may be missed as patient frailty deters clinicians from taking samples and diagnostic tests perform poorly in this group. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) This places greater importance on syndromic or symptom-based diagnoses to detect and treat cases promptly, avoid secondary transmission, and inform public health interventions to reduce disease burden.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eTo identify LRTI, standardised case definitions are used which typically include fever, cough, and other signs of respiratory disease such as increased/abnormal sputum production. However, these symptoms may not manifest in older age.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Age can directly influence the way LRTI presents as immunosenescence results in a less robust immune response,(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and as lung function declines.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) In addition, chronic medical conditions accumulate through life and can mask the clinical features of infection,(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) while dementia and cognitive impairment are increasingly common in older adults and may prevent people from articulating their symptoms.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eOlder adults account for a growing proportion of the global population therefore detecting LRTI in this vulnerable group is a public health priority.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) We investigated whether syndromic case ascertainment was different in older adults compared to younger adults, by analysing LRTI symptom profiles of cases recruited to an ongoing prospective cohort study in Bristol, UK.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003e The study was approved by the Health Research Authority Research Ethics Committee East of England, Essex, reference 20/EE/0157. Informed consent was obtained from cognisant patients, and declarations for participation from consultees for individuals lacking capacity. Patients who declined consent were not included in this analysis. For individuals for whom an approach to seek consent could not be made, data were included with approval from the Clinical Advisory Group (20/CAG/0138).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eAdults (\u0026ge;\u0026thinsp;18y) admitted to both acute care hospitals in Bristol, UK, were recruited to the AvonCAP study (ISRCTN:17354061) if they had\u0026thinsp;\u0026ge;\u0026thinsp;2 signs/symptoms compatible with acute lower respiratory tract disease (aLRTD), or a clinical or radiological aLRTD diagnosis (\u003cb\u003eSupplementary data 1\u003c/b\u003e). The study protocol is published containing full recruitment details.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) Those with a confirmed diagnosis of lower respiratory tract infection (LRTI) who were hospitalised between 1st August 2020 and 31st July 2022 were included in this analysis.\u003c/p\u003e \u003cp\u003eConfirmed LRTI was defined as evidence of active infection plus aLRTD, or a clinician diagnosis of LRTI, or a positive laboratory or radiological test for respiratory infection (\u003cb\u003eSupplementary data 1\u003c/b\u003e). LRTI were classed as pneumonia if there were confirmed radiological changes compatible with infection, or when the treating clinician diagnosed pneumonia.(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) SARS-CoV-2 infection was defined as lower respiratory tract disease and a positive test result for SARS-CoV-2 on/during hospitalisation or within 7-days prior to hospital admission, using the established UK Health Security Agency (UKHSA) diagnostic assay deployed at the time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eDemographic and clinical data were systematically collected from patient records using REDCap.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) They included a Rockwood clinical frailty score (a score\u0026thinsp;\u0026gt;\u0026thinsp;4 indicates frailty), ranging from people who need help with higher order instrumental activities of daily living (IADL) to those who are completely dependent.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) Comorbidity was assessed using the Charlson Comorbidity Index (CCI)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) excluding scores assigned for a diagnosis of dementia (which were recorded separately), with CCI scores categorised into none (CCI\u0026thinsp;=\u0026thinsp;0), mild (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), moderate (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) or severe (\u0026gt;\u0026thinsp;4). Disease severity was measured using the CRB-65 score(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) on admission, with a point assigned for each of: acute confusion (Abbreviated Mental Test Score\u0026thinsp;\u0026le;\u0026thinsp;7); raised respiratory rate (\u0026ge;\u0026thinsp;30) and low blood pressure (systolic\u0026thinsp;\u0026lt;\u0026thinsp;90mmHg or diastolic\u0026thinsp;\u0026le;\u0026thinsp;60mmHg). As no points were assigned for age\u0026thinsp;\u0026ge;\u0026thinsp;65y since this was our outcome of interest, we term this the CRB score.\u003c/p\u003e \u003cp\u003eThe results of standard-of-care laboratory (virological and/or bacteriological) tests, chest radiology and clinical findings were also recorded. A respiratory physician reviewed all patient records, including clinician notes and investigation results, to validate the final clinical diagnosis according to pre-specified diagnostic criteria (\u003cb\u003eSupplementary data 1\u003c/b\u003e) .\u003c/p\u003e \u003cp\u003eA syndromic case definition for suspected LRTI was constructed using signs and symptoms publicised to both the public and clinicians by the National Health Service (NHS) and British Medical Journal (BMJ), respectively (\u003cb\u003eSupplementary data 2\u003c/b\u003e).(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) To meet this case definition, patients had to present with cough plus fever (reported fever/chills or temperature\u0026thinsp;\u0026gt;\u0026thinsp;38.0\u0026deg;C or \u0026lt;\u0026thinsp;35\u0026deg;C), or with at least three of the following: cough, fever, breathlessness, wheeze, pleurisy (chest pain on breathing), abnormal sputum production, myalgia, headache, or general deterioration (weakness/fatigue/anorexia).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy objectives\u003c/h2\u003e \u003cp\u003eThe primary objective was to determine whether older adults hospitalised with LRTI were less likely to meet the syndromic LRTI case definition when compared to younger adults. Secondary objectives were: a) describe the cohort of adults with LRTI by factors that influence clinical presentation (age, sex, levels of comorbidity/frailty, and diagnosis of dementia or cognitive impairment), and b) assess which symptom profiles characterised older adults.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical data were summarised as counts and percentages, continuous data as medians with interquartile (IQR) ranges. The characteristics of patient groups were compared using Fisher exact tests for dichotomous variables, two-sided Kolmogorov-Smirnov tests for continuous variables and Wilcoxon rank sum tests for score variables. Density plots were used to visualise changes in cases\u0026rsquo; age distribution and SARS-CoV-2 positivity over time, to better understand sources of bias.\u003c/p\u003e \u003cp\u003eTo assess whether LRTI case ascertainment was lower in adults\u0026thinsp;\u0026ge;\u0026thinsp;65y compared to those\u0026thinsp;\u0026lt;\u0026thinsp;65y we built a multivariable logistic regression model. The primary exposure was age\u0026thinsp;\u0026ge;\u0026thinsp;65y, and the outcome was meeting the syndromic LRTI case definition at presentation (Yes/No). Covariates were selected based on our understanding of relationships between age, LRTI symptom expression and associated factors (\u003cb\u003eSupplementary data 4\u0026ndash;5\u003c/b\u003e), along with results of our descriptive analyses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary data 6). We then stratified the analysis by levels of comorbidity (CCI\u0026thinsp;\u0026gt;\u0026thinsp;4 versus \u0026le;\u0026thinsp;4), followed by SARS-CoV-2 positivity, and tested sensitivity to changes in the case definition by assessing probability of cases presenting with any diagnostic symptom.\u003c/p\u003e \u003cp\u003eTo explore differences in symptom expression we calculated the posterior log-odds of each symptom being expressed in adults\u0026thinsp;\u0026ge;\u0026thinsp;65y versus adults\u0026thinsp;\u0026lt;\u0026thinsp;65y, employing an empirical Bayesian approach with a multinomial model and an informative Dirichlet prior estimated from the data. To account for greater variance in estimates for rarer symptoms, log-odds were weighted according to the frequency of each symptom observed and presented as z-scores of the log odds ratio.\u003c/p\u003e \u003cp\u003eTo assess whether symptom profiles changed with age, patterns of symptom expression were shown as heatmaps with patients grouped across 10-year age bands and cells coloured according to symptom frequency within each age band, relative to all other symptoms within that band. A dendrogram grouped age bands based on a Euclidean distance matrix, showing which were most closely related in terms of symptom profile. To illustrate this further a k-means clustering algorithm was applied,(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) identifying which 10y age bands were most closely related in terms of symptom profiles, and age bands were assigned to clusters such that the sum of squared distances between age bands and cluster centroid was minimised. Principal Component Analysis was then conducted on LRTI symptoms, creating linear combinations (Principal Components, [PC]), such that each PC contributed to overall variance but was uncorrelated with other PCs.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) These elicited combinations of symptoms (symptom profiles) that explained most of the differences between 10y age bands. Finally, the k-means clusters were presented graphically with age bands positioned according to their first and second PC values, such that age bands with similar symptom profiles appear close together and those that differ, further apart.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R statistical software version 4.2.1.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Missing data were limited to CRB score and comorbidity level variables - each accounting for 0.06% of the sample. No imputation was performed and multivariable logistic regression analyses only included participants with complete data. Statistical significance was defined using a 2-sided significance level of α\u0026thinsp;=\u0026thinsp;0∙05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOverall, 21,447 adults were hospitalised with aLRTD symptoms. \u0026nbsp;A total of 17,620 (82.0%) were diagnosed with LRTI including 7,310 (41.5%) with a positive SARS-CoV-2 test and 10,125 (57.5%) with pneumonia. Among confirmed LRTI cases, 8,487 (48.1%) had symptoms that met the syndromic LRTI case definition (\u003cstrong\u003eSupplementary data 3\u003c/strong\u003e). These cases were younger, less likely to live in a care home and less likely to have received the SARS-CoV-2 vaccine or have active SARS-CoV-2 infection. They \u0026nbsp;were also less likely to have severe levels of comorbidity (CCI\u0026gt;4), a diagnosis of dementia/cognitive impairment, or at least one CRB score indicator of severe disease on admission as compared to cases whose symptoms did not meet the syndromic LRTI case definition (\u003cstrong\u003eTable1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe age distribution of SARS-CoV-2 positive cases changed over time, with a peak emerging in late 2020 through 2021\u0026nbsp;which\u0026nbsp;represented\u0026nbsp;younger cases with symptoms largely meeting the\u0026nbsp;syndromic LRTI\u0026nbsp;case definition. There was also a peak in older cases, most of whom did not have symptoms meeting the case definition, which was consistent except in quarter 2 of 2021 (\u003cstrong\u003eFigure 1C\u003c/strong\u003e). From mid-2021 older cases began to dominate, eventually mirroring the age distribution for non-SARS-CoV-2 LRTI (\u003cstrong\u003eFigure 1B\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe probability of symptoms meeting the syndromic LRTI case definition was significantly lower for adults aged \u0026ge;65y as compared to adults aged \u0026lt;65y both on univariable analysis (OR=0.62, 95% Confidence Interval (CI) 0.58-0.66) and after adjusting for sex, dementia/ cognitive impairment, and severe levels of comorbidity (aOR=0.67, 95%CI 0.63-0.71; \u003cstrong\u003eTable 2\u003c/strong\u003e).\u0026nbsp;The presence of dementia/cognitive impairment also significantly reduced the probability of meeting the syndromic case definition (aOR=0.60, 95%CI 0.54-0.66). Stratified analysis yielded very similar results for cases with CCI\u0026le;4, whereas among cases with severe levels of comorbidity (CCI\u0026gt;4) there was no evidence of age or dementia influencing the probability of meeting the syndromic case definition (\u003cstrong\u003eSupplementary data 7\u003c/strong\u003e).\u0026nbsp;When we stratified by SARS-CoV-2 positivity and added SARS-CoV-2 vaccination status as a covariate (see DAG in\u003cstrong\u003e\u0026nbsp;Supplementary Data 4\u003c/strong\u003e), having had \u0026ge;1 dose of vaccine lowered the probability of meeting the case definition for SARS-CoV-2 positive cases (aOR=0.45, 95%CI 0.41-0.50) but increased it for SARS-CoV-2 negative cases (aOR=1.41, 95%CI 1.29-1.53). Age \u0026ge;65y reduced the probability of meeting the case definition with an attenuated effect in SARS-CoV-2 LRTI as compared to other LRTI (aOR=0.68, 95%CI 0.61-0.75 versus aOR=0.57, 95%CI 0.52-0.62); dementia/cognitive impairment had the same effect in both groups. In contrast to the full LRTI cohort, among SARS-CoV-2 positive cases male sex significantly increased the probability of meeting the syndromic case definition (aOR=1.29, 95%CI 1.17-1.43), and severe levels of comorbidity had no significant effect (\u003cstrong\u003eSupplementary Data 8)\u003c/strong\u003e. \u0026nbsp;As a sensitivity analysis we assessed probability of cases expressing any one of the symptoms in the syndromic analysis and found no difference by age, sex, or comorbidity level. Only the presence of dementia/ cognitive impairment reduced the probability of any symptom being reported (aOR=0.58, 95%CI 0.51-0.67; \u003cstrong\u003eSupplementary Data 9\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOlder adults (\u0026ge;65y) were more likely to present with confusion, falls, and general deterioration and less likely to present with pleurisy, headache, cough, and sputum than younger adults (\u0026lt;65y) (log-odds of symptom expression, \u003cstrong\u003eFigure 2A\u003c/strong\u003e). Analysis of symptom profiles by 10y age bands showed that cough and breathlessness were the most frequent presenting symptoms across the age spectrum,\u0026nbsp;except for\u0026nbsp;cases \u0026ge;85y who were less likely to cough and more likely to experience confusion and falls (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). The heatmap revealed two distinct clusters separating cases above and below 55y, with fever and pleurisy more prominent below 55y. \u0026nbsp;This clustering was also apparent when assessing 10y age bands by k-means clustering (\u003cstrong\u003eFigure 2C\u003c/strong\u003e), with the youngest (18-24y) and oldest (\u0026ge;85y) age bands lying furthest from all others.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Characteristics of LRTI cases by whether presenting symptoms meet the case definition.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"573\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot meeting LRTI case definition*\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eN = 9,133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeeting LRTI case definition*\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eN = 8,487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003eAge (yrs) at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e75 (58, 85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e68 (52, 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;18-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e189 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e254 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e506 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e492 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e549 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e691 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;45-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e681 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e958 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;55-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e1,051 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e1,327 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;65-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e1,486 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e1,641 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;75-84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e2,301 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e1,851 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e2,370 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e1,273 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003eSARS-CoV-2 test positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e4,259 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e3,051 (36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003eVaccinated against SARS-CoV-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e5,524 (65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e4,865 (61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003eCRB score\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e5,853 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e5,857 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e2,731 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e2,269 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e501 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e332 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e41 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e25 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e4,601 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e4,353 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003eCare home resident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e910 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e482 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003eDementia/ cognitive impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e1,285 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e668 (7.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003eClinically frail\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e3,860 (55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e2,785 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e2,145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e1,329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003eComorbidity level\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e3,640 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e3,449 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Mild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e3,707 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e3,503 (41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e1,245 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e1,137 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e534 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e394 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.80487804878049%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.912891986062718%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.21602787456446%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.066202090592334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e* Median (IQR); n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e** Welch Two Sample t-test; Wilcoxon rank sum test; Fisher\u0026apos;s Exact Test for Count Data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003ePneumonia severity score, 1 point assigned for each of:\u0026nbsp;\u003cbr\u003e\u0026nbsp; \u0026nbsp;acute confusion, raised \u0026nbsp;respiratory rate, low blood pressure.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eRockwood frailty score \u0026gt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Charlson Comorbidity Index minus age and dementia scores.\u003cbr\u003e\u0026nbsp; \u0026nbsp;mild: 1-2, moderate: 3-4, severe: \u0026gt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Logistic Regression - odds of symptoms meeting LRTI case definition.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.462809917355372%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"41.15702479338843%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted odds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.3801652892562%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted odds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.462809917355372%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.917355371900827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.462809917355372%\" valign=\"top\"\u003e\n \u003cp\u003eAged \u0026gt;=65y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.917355371900827%\" valign=\"top\"\u003e\n \u003cp\u003e17,620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e0.58, 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e0.63, 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.462809917355372%\" valign=\"top\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.917355371900827%\" valign=\"top\"\u003e\n \u003cp\u003e17,620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e0.98, 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e0.99, 1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.462809917355372%\" valign=\"top\"\u003e\n \u003cp\u003eDementia/ cognitive impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.917355371900827%\" valign=\"top\"\u003e\n \u003cp\u003e17,620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e0.47, 0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e0.54, 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.462809917355372%\" valign=\"top\"\u003e\n \u003cp\u003eCCI score \u0026gt;4 (severe)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.917355371900827%\" valign=\"top\"\u003e\n \u003cp\u003e17,609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.768595041322314%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e0.69, 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.561983471074381%\" valign=\"top\"\u003e\n \u003cp\u003e0.75, 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.909090909090908%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e* OR = Odds Ratio, CI = Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eCharlson Comorbidity Index minus age and dementia scores.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eLess than half of adults hospitalised with LRTI presented with symptoms matching the syndromic LRTI case definition. Our results suggest that symptom profiles publicised to the UK public and clinicians characterise cases of LRTI among young, healthy adults but may fail to identify cases among older and frailer adults, particularly those with dementia or cognitive impairment who may not express any diagnostic symptoms. People with severe comorbidities (CCI\u0026thinsp;\u0026gt;\u0026thinsp;4) are also at high risk of a missed diagnosis, regardless of age. We find that cases aged\u0026thinsp;\u0026ge;\u0026thinsp;65y, who made up almost two thirds of the cohort, were less likely to present with classical LRTI symptoms such as cough, fever and pleurisy when compared to younger cases, and that those aged\u0026thinsp;\u0026ge;\u0026thinsp;85y commonly presented with confusion and falls. This has implications for older patients whose infections may not be diagnosed and treated, as well as research and policy since standard screening protocols may underestimate disease burden and vaccine effectiveness in older age groups.\u003c/p\u003e \u003cp\u003eThe AvonCAP prospective cohort study provided clinically validated LRTI diagnoses and comprehensive symptoms data with which we could assess the performance of syndromic case definitions. Few studies have assessed the sensitivity of LRTI case definitions by age group or explored how symptom profiles evolve with age. This analysis has highlighted that presenting symptoms are not reliable predictors of LRTI in older patients, and identified symptom profiles which may help hospital clinicians to identify and treat cases of LRTI in this population. Researchers may also wish to modify LRTI diagnostic/screening tools to include the full spectrum of clinical presentations.\u003c/p\u003e \u003cp\u003eOther studies of hospitalised adults have reported low sensitivity for LRTI case definitions in older adults;(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) in a study of veterans aged\u0026thinsp;\u0026gt;\u0026thinsp;40y with bacterial pneumonia, younger adults (\u0026lt;\u0026thinsp;65y) were significantly more likely to present with breathlessness, sputum production and pleurisy than older adults (\u0026ge;\u0026thinsp;65y).(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Confusion and falls were common among veteran cases aged\u0026thinsp;\u0026gt;\u0026thinsp;80y, which was attributed to high rates of dementia, but contrary to our findings, cough was as prevalent in this age group as in others.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Studies focussing on hospitalised adults aged\u0026thinsp;\u0026gt;\u0026thinsp;80y have reported much higher prevalence of altered mental state (53\u0026ndash;77%) and lower prevalence of cough (40\u0026ndash;63%) in nursing home-acquired pneumonias as compared to community-acquired pneumonias (altered mental state: 12\u0026ndash;45%, cough: 49\u0026ndash;81%).(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) This symptom profile may therefore be characteristic of frailer cases (frailty defined as a degree of dependence for daily activities of living),(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) since cases aged\u0026thinsp;\u0026ge;\u0026thinsp;85y accounted for the greatest proportion (17%) of care home residents in our cohort.\u003c/p\u003e \u003cp\u003eWith respect to SARS-CoV-2 LRTI, which made up over 40% of our sample, data from the UK Coronavirus (COVID-19) Infection Survey also show that the probability of reporting fever declines from around the age of 60y.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) Although fever is considered a cardinal symptom of LRTI it is a poor predictor of infection in older adults,(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) and its absence will contribute to case under-ascertainment. The bimodal age distribution of SARS-CoV-2 positive cases from the end of 2020 through to 2022 is likely to reflect the emergence of new variants Alpha and Delta while older adults were prioritised for vaccination. This resulted in a greater proportion of hospitalised cases among younger adults,(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) whilst the steady peak in older aged cases represents frail individuals with a low threshold for hospital admission. The age distribution of sexes also differed for SARS-CoV-2 when compared to other forms of LRTI (\u003cb\u003eSupplementary data 10\u003c/b\u003e), with a peak in young females possibly representing pregnant women. Assuming these individuals had a lower severity threshold for admission, they would have been less likely to express symptoms and meet the LRTI case definition, which could explain why male sex was associated with meeting the case definition for SARS-CoV-2 positive cases (\u003cb\u003eSupplementary data 7\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThis analysis has limitations including shifts in case mix and symptom profiles over the study period as the result of time-varying factors which we did not account for, and which may have biased results of the regression analysis. These factors include seasonal fluctuations in respiratory illnesses, the emergence of SARS-CoV-2 variants, implementation of lockdowns and changes in hospital admission thresholds for care home residents and other vulnerable groups.(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) Additionally, older adults were prioritised for SARS-CoV-2 vaccination, which reduced severity of illness and therefore symptom expression and may have enhanced the effect of older age on probability of meeting the case definition,(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) although the effect remained strong when we conditioned on vaccination status in the SARS-CoV-2 stratification. Our regression model was designed to estimate the total effects of older age on symptom profile and did not allow us to disentangle the effects of age, frailty, vaccination, and severity of illness (see DAG in \u003cb\u003eSupplementary Data 4\u003c/b\u003e). The SARS-CoV-2 virus was prioritised for testing above other pathogens during the study period, so other causes of LRTI may have been underestimated, nonetheless the majority of LRTI in our cohort and a previous AvonCAP cohort were SARS-CoV-2 negative.(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) A limitation of the AvonCAP study is that it is restricted to hospitalised cases of LRTI. Since these are likely to have more severe illness and/ or to be frailer than cases in the community, our results are not generalisable to LRTI outside the hospital setting. Data were gathered retrospectively from hospital case notes so misclassification in clinical syndromes and diagnoses is possible, and our cohort may not be representative of all hospitalised LRTI. However, enrolment criteria were broad and case review thorough to minimise risk of false negatives. Finally, this is not an evaluation of clinical diagnostic tools; our syndromic case definitions are constructs used to assess whether older adults with LRTI are less likely to be diagnosed at presentation, based on commonly known symptoms.\u003c/p\u003e \u003cp\u003eIn conclusion, this analysis provides further evidence that older adults with LRTI present atypically, reducing the likelihood of timely diagnosis and successful intervention. Age-specific case definitions could improve case ascertainment and patient outcomes, although further investigation is needed to determine symptom profiles in non-hospitalised cases of LRTI, including those with cognitive impairment and living in long-term care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur thanks to colleagues at the University of Bristol for their support with this study, including Rachel Davies, Paul Savage, Emma Foose, Susan Christie, Mark Mumm\u0026eacute;, and Adam Taylor, and to Gibran Hemani for his advice on causal diagrams. Also, to Kevin Sweetland and Aman Kaur-Singh in the AvonCAP team. We would also like to acknowledge the research teams at North Bristol and University Hospitals of Bristol and Weston NHS Trusts for making this study possible, including Helen Lewis-White, Rebecca Smith, Rajeka Lazarus, Mark Lyttle, Kelly Turner, Jane Blazeby, Diana Benton, and David Wynick. We would also like to acknowledge Christian Theilacker and Maria Lahuerta for suggestions on the manuscript and Bradford Gessner, Jo Southern and Elizabeth Begier for assistance with setting up the AvonCAP study. We acknowledge the invaluable contributions of Alison Horne, Mai Baquedano, Stewart Robinson, David Clint, and Henry Stuart. A big thank you to all our study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe AvonCAP Research Group:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnna Morley, Amelia Langdon, Anabella Turner, Anya Mattocks, Bethany Osborne, Charli Grimes, Claire Mitchell, David Adegbite, Emma Bridgeman, Emma Scott, Fiona Perkins, Francesca Bayley, Gabriella Ruffino, Gabriella Valentine, Grace Tilzey, James Campling, Johanna Kellett Wright, Julia Brzezinska, Julie Cloake, Katarina Milutinovic, Kate Helliker, Katie Maughan, Kazminder Fox, Konstantina Minou, Lana Ward, Leah Fleming, Leigh Morrison, Lily Smart, Louise Wright, Lucy Grimwood, Maddalena Bellavia, Madeleine Clout, Marianne Vasquez, Maria Garcia Gonzalez, Milo Jeenes-Flanagan, Natalie Chang, Niall Grace, Nicola Manning, Oliver Griffiths, Pip Croxford, Peter Sequenza, Rajeka Lazarus, Rhian Walters, Robin Marlow, Robyn Heath, Rupert Antico, Sandi Nammuni Arachchge, Seevakumar Suppiah, Taslima Mona, Tawassal Riaz, Vicki Mackay, Zandile Maseko, Zoe Taylor, Zsolt Friedrich, Zsuzsa Szasz-Benczur.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA SHARING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo preserve the confidentiality of our participants the data from this study cannot be made publicly available. The code used for this analysis is available on GitHub: \u0026nbsp;https://github.com/bristol-vaccine-centre/LRTI_symptoms .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRK, LD, AC and CH conceived the research question and developed the analysis plan. CH, AM, JK, MC, and The AvonCAP team were involved in data collection and RK and AC analysed the data. All authors contributed to interpretation of results and commented on the manuscript written by RK. AF provided oversight of the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATIONS OF INTEREST/ CONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCH is Principal Investigator of the AvonCAP study, a University of Bristol sponsored study which is funded by Pfizer. JO is a Co-Investigator on the AvonCAP Study. AF is a member of the UK Dept of Health, Joint Committee on Vaccination and Immunization (JCVI) and, until December 2022, was chair of the World Health Organization European Technical Advisory Group of Experts on Immunization (ETAGE). In addition to receiving funding for this study from Pfizer as Chief Investigator, he leads another project investigating transmission of respiratory bacteria in families jointly funded by Pfizer and the Gates Foundation and is chief or principal investigator in current or recent COVID-19 vaccine trials funded by Astra-Zeneca, Valneva and Sanofi. LD, RC are also partly funded through AvonCAP. LD is a Co-Investigator of the AvonCAP study and has also received funding from Pfizer, UKRI and UKHSA for unrelated projects. RK holds an honorary contract with the UK Health Security Agency (UKHSA). The corresponding author had full access to all data in the study and final responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe AvonCAP study is sponsored by the University of Bristol, funded under an investigator-led collaborative agreement by Pfizer Inc. The funder had no role in data collection or design of this study. CH and RK have previously held NIHR Academic Clinical Fellowships. RK is now funded by the Wellcome GW4 Clinical Academic Training programme [203918].\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKyu HH, Vongpradith A, Sirota SB, Novotney A, Troeger CE, Doxey MC et al. Age-sex differences in the global burden of lower respiratory infections and risk factors, 1990\u0026ndash;2019: results from the Global Burden of Disease Study 2019. 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Lancet Reg Heal - Eur. 2023;25:100556.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Respiratory tract infections, pneumonia, public health surveillance, missed diagnosis, age factors.","lastPublishedDoi":"10.21203/rs.3.rs-3933825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3933825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLower Respiratory Tract Infections (LRTI) pose a serious threat to older adults but may be underdiagnosed due to atypical presentations. Here we assess LRTI symptom profiles and syndromic (symptom-based) case ascertainment in older (≥65y) as compared to younger adults (\u0026lt;65y).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe included adults (≥18y) with confirmed LRTI admitted to two acute care Trusts in Bristol, UK from 1st August 2020- 31st July 2022. \u0026nbsp;Logistic regression was used to assess whether age ≥65y reduced the probability of meeting syndromic LRTI case definitions, using patients’ symptoms at admission. We also calculated relative symptom frequencies (log-odds ratios) and evaluated how symptoms were clustered across different age groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf 17,620 clinically confirmed LRTI cases, 8,487 (48.1%) had symptoms meeting the case definition. Compared to those not meeting the definition these cases were younger, had less severe illness and were less likely to have received a SARS-CoV-2 vaccination or to have active SARS-CoV-2 infection. Prevalence of dementia/cognitive impairment and levels of comorbidity were lower in this group.\u003c/p\u003e\n\u003cp\u003eAfter controlling for sex, dementia and comorbidities, age ≥65y significantly reduced the probability of meeting the case definition (aOR=0.67, 95% CI:0.63-0.71). Cases aged ≥65y were less likely to present with fever and LRTI-specific symptoms (e.g., pleurisy, sputum) than younger cases, and those aged ≥85y were characterised by lack of cough but frequent confusion and falls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLRTI symptom profiles changed considerably with age in this hospitalised cohort. Standard screening protocols may fail to detect older and frailer cases of LRTI based on their symptoms.\u003c/p\u003e","manuscriptTitle":"Syndromic case definitions for Lower Respiratory Tract Infection (LRTI) are less sensitive in older age: an analysis of symptoms among hospitalised adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-13 18:52:03","doi":"10.21203/rs.3.rs-3933825/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-01T16:17:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-15T08:30:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2f42ab08-2ddd-4641-807e-fe5478ece50c","date":"2024-02-14T07:30:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-14T06:11:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-14T06:05:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-14T05:23:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-14T05:19:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-02-06T12:19:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"498e406d-8d01-467d-9200-0e7e94153262","owner":[],"postedDate":"February 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-21T13:51:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-13 18:52:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3933825","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3933825","identity":"rs-3933825","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}