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6. Symptoms and risk factors for long COVID in non-hospitalized adults

Author

Subramanian, Anuradhaa, Nirantharakumar, Krishnarajah, Hughes, Sarah, Myles, Puja, Williams, Tim, Gokhale, Krishna M., Taverner, Tom, Chandan, Joht Singh, Brown, Kirsty, Simms-Williams, Nikita, Shah, Anoop D., Singh, Megha, Kidy, Farah, Okoth, Kelvin, Hotham, Richard, Bashir, Nasir, Cockburn, Neil, Lee, Siang Ing, Turner, Grace M., Gkoutos, Georgios V., Aiyegbusi, Olalekan Lee, McMullan, Christel, Denniston, Alastair K., Sapey, Elizabeth, Lord, Janet M., Wraith, David C., Leggett, Edward, Iles, Clare, Marshall, Tom, Price, Malcolm J., Marwaha, Steven, Davies, Elin Haf, Jackson, Louise J., Matthews, Karen L., Camaradou, Jenny, Calvert, Melanie, and Haroon, Shamil

Published
2022
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16. Reply to Jones et al

Author

Venkatesan, Sudhir, Myles, Puja R., Memish, Ziad, and Nguyen-Van-Tam, Jonathan S.

Published
2017

18. A study of the impact of statins, ACE inhibitors and gastric acid suppressants on pneumonia risk and mortality using the Health Improvement Network Database

Author

Myles, Puja Runa

Subjects
610.21, WC Communicable diseases
Abstract

Pneumonia is a common diagnosis in general practice and is associated with significant morbidity and mortality. Current estimates of pneumonia incidence in the UK are based on studies more than a decade ago and little is known about longer term outcomes in pneumonia patients. Though much is known about the aetiology of pneumonia and predictors of mortality, an emerging area for research is the relationship between commonly prescribed drugs in general practice and pneumonia. The aims of this thesis were first, to determine overall incidence and mortality for pneumonia and how these vary by socio-demographic characteristics like age, sex, deprivation; and second, to investigate whether statins, angiotensin converting enzyme inhibitors (ACEIs) and gastric acid suppressants like proton pump inhibitors (PPIs) and histamine 2 receptor antagonists (H2RAs) modify the risk of acquiring pneumonia and its prognosis. This study used data from The Health Improvement Network (THIN) database, a longitudinal database of anonymised computerised medical patient records from 330 United Kingdom (UK) general practices at the time of data extraction in 2006. A cohort design was used to determine pneumonia incidence and mortality in the UK. Case-control, case-series and cohort study designs were used to investigate associations between the various drug exposures and pneumonia. The overall incidence of pneumonia was 237 per 100,000 person-years (95 % confidence interval (CI): 235 to 239) and this rate was stable between 1991 and 2003. Pneumonia was more common in men and in children under the age of four years and adults over the age of 65 years. There was an increased incidence of pneumonia with higher levels of socioeconomic disadvantage. Pneumonia cases showed much higher all-cause mortality as compared to the general population, both in the short and long-term and this increase was independent of underlying comorbidity. After adjusting for potential confounders, current prescriptions for statins and ACE inhibitors were associated with a significant reduction in the risk of acquiring pneumonia. Current prescriptions for PPIs were associated with an increased risk of pneumonia. With regards the impact on mortality: the use of statins was associated with a lower risk of short and long-term mortality following pneumonia whereas the use of ACEIs was associated with a decreased mortality risk only in the short-term. No relationship was observed between prescriptions for PPIs, H2RAs and pneumonia mortality. This study shows that caution must be exercised while prescribing proton pump inhibitors especially in patients known to be at high risk of pneumonia. There is also a potential role for statins in preventing pneumonia in at-risk patients and improving pneumonia outcomes but this will necessitate clinical trials to determine adequate dose, duration and safety profiles before any prescribing policy recommendations are made.

Published
2009

19. Impact of Outpatient Neuraminidase Inhibitor Treatment in Patients Infected With Influenza A(H1N1)pdm09 at High Risk of Hospitalization: An Individual Participant Data Metaanalysis

Author

Venkatesan, Sudhir, Myles, Puja R., Leonardi-Bee, Jo, Muthuri, Stella G., Al Masri, Malak, Andrews, Nick, Bantar, Carlos, Dubnov-Raz, Gal, Gérardin, Patrick, Koay, Evelyn S. C., Loh, Tze Ping, Memish, Ziad, Miller, Elizabeth, Oliva, Maria E., Rath, Barbara A., Schweiger, Brunhilde, Tang, Julian W., Tran, Dat, Vidmar, Tjasa, Waight, Pauline A., and Nguyen-Van-Tam, Jonathan S.

Published
2017

26. Quality of rheumatoid arthritis recording in United Kingdom Clinical Practice Research Datalink Aurum.

Author

Vasilakis‐Scaramozza, Catherine, Hagberg, Katrina Wilcox, Persson, Rebecca, Yelland, Eleanor, Williams, Tim, Myles, Puja, and Jick, Susan S.

Abstract

Purpose: While several studies have assessed quality and completeness of recording acute medical events in Clinical Practice Research Datalink (CPRD) Aurum, evaluation of additional chronic conditions is warranted. Methods: We selected patients with a first diagnosis of rheumatoid arthritis (RA) coded in their CPRD Aurum record between 2005 and 2019. We assessed quality of RA diagnosis by evaluating additional information in the patient record that would corroborate the diagnosis. We report recording of diagnoses, prescriptions, labs, and referrals expected to be present based on NICE guidelines for RA management. Results: There were 53 083 patients with a first recorded RA diagnosis during the study period: 43606 (82%) patients had RA drug treatments in their record, 7596 (14%) had supporting codes without drug treatment, and 1881 (4%) patients had only a RA diagnoses recorded in their medical record with no supporting codes or RA treatments. Patients with RA diagnosis only were more likely to be first diagnosed in the earliest time period of study. Labs for diagnosing and monitoring RA were most common among patients with RA treatment. Analgesic and glucocorticoid prescriptions were common in all study patients but were highest among patients with RA treatment. Among patients with RA diagnosis only, the overwhelming majority had only one RA diagnosis recorded (76%). Conclusions: Our findings suggest that codes expected for monitoring and treatment of RA are routinely recorded in CPRD Aurum. These results support previous assessments, which found data recorded in CPRD Aurum to be of good quality for use in research. [ABSTRACT FROM AUTHOR]

Published
2023
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29. Presence of Codes for Indication for Use in Clinical Practice Research Datalink Aurum: An Assessment of Benign Prostatic Hyperplasia Treatments.

Author

Persson, Rebecca, Hagberg, Katrina Wilcox, Vasilakis-Scaramozza, Catherine, Yelland, Eleanor, Williams, Tim, Myles, Puja, and Jick, Susan S

Subjects
BENIGN prostatic hyperplasia, CLINICAL indications, MEDICAL research, MEDICAL coding, REDUCTASE inhibitors
Abstract

Background: Assessments of strengths and limitations of new data sources are critical for making decisions about suitability for specific research questions. For some studies, it is necessary to capture a drug's indication for use. Objective: To assess the presence of indications for prescription use in Clinical Practice Research Datalink (CPRD) Aurum (January 1988–June 2021) by describing the proportion of men in CPRD Aurum who had a recorded indication for use of prescriptions for 5-alpha reductase inhibitors (5-ARI), alpha blockers (AB), or tadalafil, which have multiple indications. Methods: From a random sample of 154 practices of CPRD Aurum data, we selected 85,597 male patients with a prescription for a 5-ARI, an AB, or tadalafil. Among these patients, we described presence of codes indicating whether the patient had benign prostatic hyperplasia, hypertension, erectile dysfunction, or alopecia using three indication definitions: narrow (specific diagnoses recorded within one year before and up to 90 days after the prescription), broad (specific diagnoses or supporting clinical codes in the time period described above), and widest (diagnoses or supporting codes recorded at any time before the prescription and up to 90 days after the prescription). Results: Using the narrow indication definition limited to diagnoses only, 39,861 (46.6%) patients' records contained an indication for use. The broad definitions, which additionally included supporting codes, captured indications for 62,912 (73.5%) patients and the widest definition, which additionally included supporting codes and all available data before the first prescription date, captured indications for 71,478 (83.5%) patients. Indications were present more often for prescriptions in 2005 and later (85.9%). Conclusion: The findings of this assessment suggest that CPRD Aurum can be used for studies that require information on treatment indications for BPH and potentially for treatments of other chronic diseases managed in the primary care setting. [ABSTRACT FROM AUTHOR]

Published
2022
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30. Nosocomial pandemic (H1N1) 2009, United Kingdom, 2009-2010

Author

Enstone, Joanne E., Myles, Puja R., Openshaw, Peter J.M., Gadd, Elaine M., Lim, Wei Shen, Semple, Malcolm G., Read, Robert C., Taylor, Bruce L., McMenamin, James, Armstrong, Colin, Bannister, Barbara, Nicholson, Karl G., and Nguyen-Van-Tam, Jonathan S.

Subjects
Vaccination -- Health aspects, Antiviral agents -- Health aspects, Influenza -- Care and treatment -- Health aspects, Adults -- Health aspects, Health
Abstract

Nosocomial influenza is a well-recognized problem in acute-care hospital settings (1,2). Outbreaks of influenza A have been reported in general wards (3,4), pediatric units (5), neonatal intensive care units (ICUs) [...]

Published
2011

33. Neuraminidase Inhibitors and Hospital Length of Stay: A Meta-analysis of Individual Participant Data to Determine Treatment Effectiveness Among Patients Hospitalized With Nonfatal 2009 Pandemic Influenza A(H1N1) Virus Infection

Author

Venkatesan, Sudhir, Muthuri, Stella G., Al Khuwaitir, Tarig, Bajjou, Tahar, Bassetti, Matteo, Beovic, Bojana, Bonmarin, Isabelle, Booy, Robert, Burgmann, Heinz, Cao, Bin, Carratala, Jordi, Chinbayar, Tserendorj, Cilloniz, Catia, Dubnov-Raz, Gal, Gao, Zhancheng, Giannella, Maddalena, Gubbels, Sophie, Herberg, Jethro, Higuera Iglesias, Anjarath Lorena, Hu, Xiao Yun, Keijzers, Gerben, Khalili, Hossein, Kusznierz, Gabriela, Kuzman, Ilija, Langenegger, Eduard, Leo, Yee-Sin, Libster, Romina P., Linko, Rita, Madanat, Faris, Maltezos, Efstratios, Mamun, Abdullah, Manabe, Toshie, Metan, Gokhan, Mickiene, Auks?, Ozkan, Mehpare, Parekh, Dhruv, Refaey, Samir, Rodriguez, Alejandro H., Sertogullarindan, Bunyamin, Skr?t-Magier?o, Joanna, Somer, Ayper, Talarek, Ewa, Tang, Julian W., Tran, Dat, Vaudry, Wendy, Vidmar, Tjasa, Zarogoulidis, Paul, PRIDE Consortium Investigators, Nguyen-Van-Tam, Jonathan S., Myles, Puja R, Bolton, Kirsty J, Anovadiya, Ashish P, Azziz-Baumgartner, Eduardo, Bertisch, Barbara, Borja-Aburto, Victor H, Denholm, Justin T, Dominguez, Samuel R, Duarte, Pericles A D, Fanella, Sergio, Hoeger, Peter H, Islam, Quazi T, Lankarani, Kamran B, Miki?, Dragan, Mohn, Kristin G I, Oliva, Maria E, Rath, Barbara A, To, Kelvin, Uyeki, Timothy M, and Khandaker, Gulam

Subjects
Infectious Diseases, Immunology and Allergy
Abstract

© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. BACKGROUND: The effect of neuraminidase inhibitor (NAI) treatment on length of stay (LoS) in patients hospitalized with influenza is unclear. METHODS: We conducted a one-stage individual participant data (IPD) meta-analysis exploring the association between NAI treatment and LoS in patients hospitalized with 2009 influenza A(H1N1) virus (A[H1N1]pdm09) infection. Using mixed-effects negative binomial regression and adjusting for the propensity to receive NAI, antibiotic, and corticosteroid treatment, we calculated incidence rate ratios (IRRs) and 95% confidence intervals (CIs). Patients with a LoS of

Published
2020

34. Association between inactivated influenza vaccine and primary care consultations for autoimmune rheumatic disease flares: a self-controlled case series study using data from the Clinical Practice Research Datalink

Author

Nakafero, Georgina, Grainge, Matthew J, Myles, Puja R, Mallen, Christian D, Zhang, Weiya, Doherty, Michael, Nguyen-Van-Tam, Jonathan S, and Abhishek, Abhishek

Subjects
rheumatoid arthritis, Male, Vasculitis, Databases, Factual, Fever, Autoimmune Diseases, RC925, Reference Values, Rheumatic Diseases, RC927, Influenza, Human, Humans, Referral and Consultation, Risk Management, Primary Health Care, Vaccination, Miscellaneous, Vaccines, Inactivated, Influenza Vaccines, Case-Control Studies, Disease Progression, Female, Patient Safety, Follow-Up Studies
Abstract

Objectives:\ud To examine the association between inactivated influenza vaccine (IIV) administration and primary care consultation for joint pain, rheumatoid arthritis (RA) flare, corticosteroid prescription, vasculitis and unexplained fever in people with autoimmune rheumatic diseases (AIRDs). \ud \ud METHODS: \ud We undertook within-person comparisons using self-controlled case-series methodology. AIRD cases who received the IIV and had an outcome of interest in the same influenza cycle were ascertained in Clinical Practice Research Datalink. The influenza cycle was partitioned into exposure periods (1-14 days prevaccination and 0-14, 15-30, 31-60 and 61-90 days postvaccination), with the remaining time-period classified as non-exposed. Incidence rate ratios (IRR) and 95% CI for different outcomes were calculated. \ud \ud RESULTS: \ud Data for 14 928 AIRD cases (69% women, 80% with RA) were included. There was no evidence for association between vaccination and primary care consultation for RA flare, corticosteroid prescription, fever or vasculitis. On the contrary, vaccination associated with reduced primary care consultation for joint pain in the subsequent 90 days (IRR 0.91 (95% CI 0.87 to 0.94)). \ud \ud CONCLUSION: \ud This study found no evidence for a significant association between vaccination and primary care consultation for most surrogates of increased disease activity or vaccine adverse-effects in people with AIRDs. It adds to the accumulating evidence to support influenza vaccination in AIRDs.

Published
2019

37. Authorsʼ response

Author

Lim, Wei Shen, Bewick, Thomas, Myles, Puja, Greenwood, Sonia, Nguyen-Van-Tam, Jonathan S, Brett, Stephen, Semple, Malcolm G, Openshaw, Peter J, Bannister, Barbara, Read, Robert C, Taylor, Bruce, McMenamin, Jim, Enstone, Joanne E, and Nicholson, Karl G

Published
2011
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42. Quality and Completeness of Myocardial Infarction Recording in Clinical Practice Research Datalink Aurum.

Author

Persson, Rebecca, Sponholtz, Todd, Vasilakis-Scaramozza, Catherine, Hagberg, Katrina Wilcox, Williams, Tim, Kotecha, Dipak, Myles, Puja, and Jick, Susan S

Subjects
MEDICAL research, HOSPITAL statistics, DIAGNOSIS, DECISION making, STATISTICAL sampling
Abstract

Background: Validation studies of the Clinical Practice Research Datalink (CPRD) Aurum database in the UK are critical for making decisions about its suitability and validity for research purposes. Objective: To examine data source agreement of myocardial infarction (MI) diagnoses recorded in CPRD Aurum compared with linked Hospital Episode Statistics (HES) data. This comparison provides information on CPRD Aurum data correctness (accuracy, validity) and completeness (presence, missingness). Methods: Patients with MI diagnoses recorded in either data source were selected from a random sample of 50,000 patients in CPRD Aurum with HES linkage (1997– 2017). Correctness was defined as the proportion of MI cases in CPRD Aurum with a concordant MI diagnosis recorded in HES or with strong supporting evidence in either data source. Completeness was defined as the proportion of patients with primary HES-coded MIs with strong supporting evidence that were also present in CPRD Aurum. Results: There were 1260 patients with MI recorded in the CPRD Aurum sample. The overall correctness of the recorded MI diagnoses was 94%: 986 patients (78%) had concordant diagnoses in HES within 90 days; 123 (10%) were concordant with HES, but with an inconclusive date and another 71 (6%) had strong supporting evidence for being a true MI case. There were 1125 patients with MI recorded in HES primary diagnosis fields with strong supporting evidence in either data source. Of these, 880 (78%) were present in CPRD Aurum, with completeness somewhat higher in more recent years. Conclusion: MI diagnoses recorded in CPRD Aurum were highly likely to be correct, supporting its use in clinical research studies. Completeness was lower, indicating the need for data linkage for some studies. [ABSTRACT FROM AUTHOR]

Published
2021
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43. Partnering for enhanced digital surveillance of influenza‐like disease and the effect of antivirals and vaccines (PEDSIDEA)

Author

Rath, Barbara, Maltezou, Helena C., Papaevangelou, Vassiliki, Papagrigoriou‐Theodoridou, Maria‐Alexandra, Alchikh, Maren, Myles, Puja, Schweiger, Brunhilde, and PEDSIDEA Network

Subjects
Pulmonary and Respiratory Medicine, Male, medicine.medical_specialty, Epidemiology, medicine.drug_class, Antibiotics, Disease, 030312 virology, Antiviral Agents, Severity of Illness Index, Cohort Studies, 03 medical and health sciences, children, Risk Factors, Internal medicine, Influenza, Human, medicine, Humans, Prospective Studies, ddc:610, Medical prescription, Prospective cohort study, mobile health, Qualitative Research, Point of care, standardization, 0303 health sciences, business.industry, Public Health, Environmental and Occupational Health, Infant, Newborn, Infant, Original Articles, Precision medicine, Hospitals, Pediatric, Infectious Diseases, Influenza Vaccines, Child, Preschool, Population Surveillance, Cohort, Original Article, Female, disease severity, ILI, business, influenza, 610 Medizin und Gesundheit, Cefuroxime, medicine.drug
Abstract

Background Standardised clinical outcome measures are urgently needed for the surveillance of influenza and influenza‐like illness (ILI) based on individual patient data (IPD). Objectives We report a multicentre prospective cohort using a predefined disease severity score in routine care. Patients/Methods The Vienna Vaccine Safety initiative (ViVI) Disease Severity Score (“ViVI Score”) was made available as an android‐based mobile application to three paediatric hospitals in Berlin and Athens between 2013 and 2016. Healthcare professionals assessed ILI patients at the point of care including severity, risk factors and use of antibiotics/antivirals/vaccines. RT‐PCR for influenza A/B viruses was performed at the Hellenic Pasteur Institute and the Robert Koch Institute. PCR testing was blinded to severity scoring and vice versa. Results A total of 1615 children aged 0‐5 years (54.4% males) were assessed at the three sites. The mean age was 1.7 years (SD 1.5; range 0‐5.9). The success rate (completion of the scoring without disruption to the ER workflow) was 100%. ViVI Disease Severity Scores ranged from 0 to 35 (mean 13.72). Disease severity in the Berlin Cohort was slightly higher (mean 15.26) compared to the Athens Cohorts (mean 10.86 and 11.13). The administration of antibiotics was most prevalent in the Berlin Cohort, with 41.2% on antibiotics (predominantly cefuroxime) as opposed to only 0.5% on neuraminidase inhibitors. Overall, Risk‐adjusted ViVI Scores were significantly linked to the prescription of both, antibiotics and antivirals. Conclusions The Risk‐adjusted ViVI Score enables a precision medicine approach to managing ILI in multicentre settings. Using mobile applications, severity data will be obtained in real time with important implications for the evaluation of antiviral/vaccine use.

Published
2019
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44. Predictors and temporal trend of flu vaccination in auto-immune rheumatic diseases in the UK: a nationwide prospective cohort study

Author

Nakafero, Georgina, Grainge, Matthew J., Myles, Puja R., Mallen, Christian, Zhang, Weiya, Doherty, Michael, Nguyen-Van-Tam, Jonathan S., and Abhishek, Abhishek

Subjects
RC925, rheumatoid arthritis, influenza, vaccination, disease modifying anti-rheumatic drugs, RA
Abstract

Objectives: To [1] examine temporal trend in uptake of seasonal influenza vaccine (SIV) in the UK, [2] explore disease and demographic factors associated with vaccination.\ud Methods: 32,751 people with auto-immune rheumatic diseases (AIRDs) prescribed disease modifying anti-rheumatic drugs (DMARDs) between 2006 and 2016 were identified from the Clinical Practice Research Datalink. The proportion vaccinated between 01/September of one year and 31/March of next year was calculated and stratified by age, other indications for vaccination, AIRD type, and number of DMARDs prescribed. Stata and Joinpoint regression programs were used.\ud Results: SIV uptake was high in those aged ≥65 years (82.3% and 80.7% in 2006-07 and 2015-16 respectively). It was significantly lower in other age groups, but improved over time with 51.9% and 61.9% in the 45-64 year age group, and 32.3% and 50.1% in the

Published
2018

45. Self-reported diseases and their associated risk factors among camp-dwelling conflict-affected internally displaced populations in Nigeria.

Author

Ekezie, Winifred, Myles, Puja, Murray, Rachael L, Bains, Manpreet, Timmons, Stephen, and Pritchard, Catherine

Subjects
DISEASE risk factors, CHRONIC disease risk factors, CLUSTER sampling, CONFIDENCE intervals, DIARRHEA, FEVER, HEALTH services accessibility, COMMUNICABLE diseases, SELF-evaluation, CROSS-sectional method, MULTIPLE regression analysis, CROWDS, DISEASES, RISK assessment, MALARIA, TYPHOID fever, MEDICAL care use, SEX distribution, REFUGEE camps, QUESTIONNAIRES, DESCRIPTIVE statistics, REFUGEES, DATA analysis software, ODDS ratio
Abstract

Background Conflict in Nigeria displaced millions of people, and some settled in camp-like locations within the country. Evidence on the association between living conditions and health outcomes among these populations are limited. This study investigated the risk factors associated with illnesses among camp-dwelling internally displaced persons (IDPs) in northern Nigeria. Methods A cross-sectional study was conducted in nine camps in 2016. Self-reported data on socio-demography, resource utilization and disease outcomes were collected. Association between health conditions and various factors, including sanitation and healthcare access, was investigated. Results Data from 2253 IDPs showed 81.1% (CI = 79.5–82.7) experienced one or more health conditions; however, over 20% did not access healthcare services. Most common diseases were malaria, fever, typhoid and diarrhoea. Multivariable logistic regression presented as adjusted odds ratios(aOR) and 95% confidence intervals(CIs) showed factors significantly associated with increased likelihood of illnesses included being female (aOR = 1.53;CI = 1.19–1.96), overcrowding (aOR = 1.07;CI = 1.00–1.36), long-term conditions (aOR = 2.72;CI = 1.88–3.94), outdoor defecation (aOR = 2.37;CI = 1.14–4.94) and presence of disease-causing vectors (aOR = 3.71;CI = 1.60–8.60). Conclusion Most diseases in the camps were communicable. Modifiable risk factors such as overcrowding and poor toilet facilities were associated with increased poor health outcomes. This evidence highlights areas of high priority when planning humanitarian public health interventions. [ABSTRACT FROM AUTHOR]

Published
2021
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46. Generating and evaluating cross‐sectional synthetic electronic healthcare data: Preserving data utility and patient privacy.

Author

Wang, Zhenchen, Myles, Puja, and Tucker, Allan

Subjects
*MEDICAL care cost statistics, *PRIMARY care, *DISEASE risk factors, *PRIVACY, *ELECTRONIC records, *MEDICAL care
Abstract

Electronic healthcare record data have been used to study risk factors of disease, treatment effectiveness and safety, and to inform healthcare service planning. There has been increasing interest in utilizing these data for new purposes such as for machine learning to develop predictive algorithms to aid diagnostic and treatment decisions. Synthetic data could potentially be an alternative to real‐world data for these purposes as well as reveal any biases in the data used for algorithm development. This article discusses the key requirements of synthetic data for multiple purposes and proposes an approach to generate and evaluate synthetic data focused on, but not limited to, cross‐sectional healthcare data. To our knowledge, this is the first article to propose a framework to generate and evaluate synthetic healthcare data with the aim of simultaneously preserving the complexities of ground truth data in the synthetic data while also ensuring privacy. We include findings and new insights from synthetic datasets modeled on both the Indian liver patient dataset and UK primary care dataset to demonstrate the application of this framework under different scenarios. [ABSTRACT FROM AUTHOR]

Published
2021
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47. Is public transport a risk factor for acute respiratory infection?

Author

Packham Christopher, Kingdon Susan, Enstone Joanne, Hashim Ahmed, Gibson Jack, Myles Puja, Troko Joy, Amin Shahid, Hayward Andrew, and Van-Tam Jonathan

Subjects
Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background The relationship between public transport use and acquisition of acute respiratory infection (ARI) is not well understood but potentially important during epidemics and pandemics. Methods A case-control study performed during the 2008/09 influenza season. Cases (n = 72) consulted a General Practitioner with ARI, and controls with another non-respiratory acute condition (n = 66). Data were obtained on bus or tram usage in the five days preceding illness onset (cases) or the five days before consultation (controls) alongside demographic details. Multiple logistic regression modelling was used to investigate the association between bus or tram use and ARI, adjusting for potential confounders. Results Recent bus or tram use within five days of symptom onset was associated with an almost six-fold increased risk of consulting for ARI (adjusted OR = 5.94 95% CI 1.33-26.5). The risk of ARI appeared to be modified according to the degree of habitual bus and tram use, but this was not statistically significant (1-3 times/week: adjusted OR = 0.54 (95% CI 0.15-1.95; >3 times/week: 0.37 (95% CI 0.13-1.06). Conclusions We found a statistically significant association between ARI and bus or tram use in the five days before symptom onset. The risk appeared greatest among occasional bus or tram users, but this trend was not statistically significant. However, these data are plausible in relation to the greater likelihood of developing protective antibodies to common respiratory viruses if repeatedly exposed. The findings have differing implications for the control of seasonal acute respiratory infections and for pandemic influenza.

Published
2011
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48. Effectiveness of inactivated influenza vaccine in autoimmune rheumatic diseases treated with disease-modifying anti-rheumatic drugs.

Author

Nakafero, Georgina, Grainge, Matthew J, Myles, Puja R, Mallen, Christian D, Zhang, Weiya, Doherty, Michael, Nguyen-Van-Tam, Jonathan S, and Abhishek, Abhishek

Subjects
INFLUENZA prevention, PNEUMONIA prevention, RESPIRATORY disease prevention, DRUG therapy for rheumatism, PNEUMONIA-related mortality, ANTIRHEUMATIC agents, AUTOIMMUNE diseases, CLINICAL trials, CONFIDENCE intervals, IMMUNIZATION, IMMUNOSUPPRESSION, INFLUENZA vaccines, LONGITUDINAL method, OBSTRUCTIVE lung diseases, RESPIRATORY infections, RHEUMATISM, TREATMENT effectiveness, PROPORTIONAL hazards models, DISEASE exacerbation, DESCRIPTIVE statistics
Abstract

Objectives The effectiveness of inactivated influenza vaccine in people with autoimmune rheumatic disease (AIRDs) is not known. We investigated whether the influenza vaccine is effective in preventing respiratory morbidity, mortality and all-cause mortality in AIRD patients. Methods Adults with AIRDs treated with DMARDs prior to 1 September of each year between 2006 and 2009, and 2010 and 2015 were identified from the Clinical Practice Research Datalink. Exposure and outcome data were extracted. Data from multiple seasons were pooled. Propensity score (PS) for vaccination was calculated. Cox-proportional hazard ratios (HRs) and 95% CIs were calculated, and were (i) adjusted, (ii) matched for PS for vaccination. Results Data for 30 788 AIRD patients (65.7% female, 75.5% with RA, 61.1% prescribed MTX) contributing 125 034 influenza cycles were included. Vaccination reduced risk of influenza-like illness [adjusted HR (aHR) 0.70], hospitalization for pneumonia (aHR 0.61) and chronic obstructive pulmonary disease exacerbations (aHR 0.67), and death due to pneumonia (aHR 0.56) on PS-adjusted analysis in the influenza active periods (IAPs). The associations were of similar magnitude and remained statistically significant on PS-matched analysis except for protection from influenza-like illness, which became non-significant. Sub-analysis restricted to pre-IAP, IAP and post-IAP did not yield evidence of residual confounding on influenza-like illness and death due to pneumonia. Vaccination reduced risk of all-cause mortality, although IAP-restricted analysis demonstrated residual confounding for this outcome. Conclusion Influenza vaccine associates with reduced risk of respiratory morbidity and mortality in people with AIRDs. These findings call for active promotion of seasonal influenza vaccination in immunosuppressed people with AIRDs by healthcare professionals. [ABSTRACT FROM AUTHOR]

Published
2020
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49. CPRD Aurum database: Assessment of data quality and completeness of three important comorbidities.

Author

Persson, Rebecca, Vasilakis‐Scaramozza, Catherine, Hagberg, Katrina Wilcox, Sponholtz, Todd, Williams, Tim, Myles, Puja, and Jick, Susan S.

Abstract

Purpose The Clinical Practice Research Datalink (CPRD) now provides a new medical record database, CPRD Aurum. This is the second of several studies being undertaken to assess the quality of CPRD Aurum data for research. Methods: We included patients aged 20+, with at least one lab test result of any type from a random sample of 50 000 patients in CPRD Aurum. We assessed whether diagnosis codes for type 2 diabetes, hyperlipidemia, and iron deficiency or unspecified anemia were accompanied by supporting codes including lab results and treatments (correctness) and whether lab results, treatments, or other codes indicate a missing diagnosis record (completeness). Results: Among 37 502 patients in CPRD Aurum, correctness of type 2 diabetes, hyperlipidemia, and anemia diagnoses was high (99%, 93%, and 97%, respectively). Completeness was only high for type 2 diabetes (94%‐98%); completeness for hypercholesterolemia and anemia diagnoses was modest even when the presence of treatments and lab results indicated the conditions were likely present (51%‐59% and 58%‐70%, respectively). Conclusions: Our findings indicate that for studies of type 2 diabetes, hyperlipidemia, and iron deficiency or unspecified anemia, the diagnosis code is likely to be correct where present. However, a significant proportion of cases of hyperlipidemia or anemia will be missed if only diagnosis codes are used to select patients with these conditions. Researchers should consider using treatments, supporting codes, and, when available, lab data to supplement diagnosis codes and enhance case capture when including these conditions in studies using CPRD Aurum. [ABSTRACT FROM AUTHOR]

Published
2020
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50. Impact of neuraminidase inhibitors on influenza A(H1N1)pdm09-related pneumonia: an individual participant data meta-analysis

Author

Muthuri, Stella G., Venkatesan, Sudhir, Myles, Puja R., Leonardi Bee, Jo, Lim, Wei Shen, Al Mamun, Abdullah, Anovadiya, Ashish P., Araújo, Wildo N., Azziz Baumgartner, Eduardo, Báez, Clarisa, Bantar, Carlos, Barhoush, Mazen M., Bassetti, Matteo, Beovic, Bojana, Bingisser, Roland, Bonmarin, Isabelle, Borja Aburto, Victor H., Cao, Bin, Carratala, Jordi, Cuezzo, María R., Denholm, Justin T., Dominguez, Samuel R., Duarte, Pericles A. D., Dubnov Raz, Gal, Echavarria, Marcela, Fanella, Sergio, Fraser, James, Gao, Zhancheng, Gérardin, Patrick, Giannella, Maddalena, Gubbels, Sophie, Herberg, Jethro, Higuera Iglesias, Anjarath L., Hoeger, Peter H., Hoffmann, Matthias, Xiaoyun, Hu, Islam, Quazi T., Jiménez, Mirela F., Kandeel, Amr, Keijzers, Gerben, Khalili, Hossein, Khandaker, Gulam, Knight, Marian, Kusznierz, Gabriela, Kuzman, Ilija, Kwan, Arthur M. C., Lahlou Amine, Idriss, Langenegger, Eduard, Lankarani, Kamran B., Leo, Yee Sin, Linko, Rita, Liu, Pei, Madanat, Faris, Manabe, Toshie, Mayo Montero, Elga, Mcgeer, Allison, Memish, Ziad A., Metan, Gokhan, Mikić, Dragan, Mohn, Kristin G. I., Moradi, Ahmadreza, Nymadawa, Pagbajabyn, Ozbay, Bulent, Ozkan, Mehpare, Parekh, Dhruv, Paul, Mical, Poeppl, Wolfgang, Polack, Fernando P., Rath, Barbara A., Rodríguez, Alejandro H., Siqueira, Marilda M., Skrȩt Magierło, Joanna, Talarek, Ewa, Tang, Julian W., Torres, Antoni, Törün, Selda H., Tran, Dat, Uyeki, Timothy M., van Zwol, Annelies, Vaudry, Wendy, Velyvyte, Daiva, Vidmar, Tjasa, Zarogoulidis, Paul, Nguyen Van Tam, Jonathan S, de Lourdes Aguiar Oliveira, Maria, Al Khuwaitir, Tarig S. A., Al Masri, Malakita, Amin, Robed, Ballester Orcal, Elena, Bao, Jing, Basher, Ariful, Bautista, Edgar, Bertisch, Barbara, Bettinger, Julie, Booy, Robert, Bouza, Emilio, Bozkurt, Ilkay, Burgmann, Heinz, Čeljuska Tošev, Elvira, Chan, Kenny K. C., Chen, Yusheng, Chinbayar, Tserendorj, Cilloniz, Catia, Cox, Rebecca J., Sarrouf, Elena B., Cui, Wei, Dashti Khavidaki, Simin, Bin, Du, El Rhaffouli, Hicham, Escobar, Hernan, Florek Michalska, Agnieszka, Gerrard, John, Gormley, Stuart, Götberg, Sandra, Honarvar, Behnam, Jianming, Hu, Kemen, Christoph, Koay, Evelyn S. C., Kojic, Miroslav, Kudo, Koichiro, Kyaw, Win M., Leibovici, Leonard, Xiao li, Li, Hongru, Li, Libster, Romina, Loh, Tze P., Macbeth, Deborough, Maltezos, Efstratios, Marcone, Debora N., Marczynska, Magdalena, Mastalir, Fabiane P., Mickiene, Aukse, Moghadami, Mohsen, Moriconi, Lilian, Oliva, Maria E., Pečavar, Blaž, Poliquin, Philippe G., Rahman, Mahmudur, Rascon Pacheco, Alberto, Refaey, Samir, Schweiger, Brunhilde, Seale, Anna C., Sertogullarindan, Bunyamin, Smith, Fang G., Somer, Ayper, Souza, Thiago M. L., Stephan, Frank, Tabarsi, Payam, Tripathi, C. B., Viasus, Diego, Qin, Yu, Zhang, Wei, Zuo, Wei, Universitat de Barcelona, Ospedale 'Santa Maria della Misericordia' = University Hospital 'Santa Maria della Misericordia', Institut de Veille Sanitaire (INVS), Beijing Chao-Yang Hospital, Beijing Institute of Respiratory Diseases, Department of physics, engineering physics and astronomy, Queen's University [Kingston, Canada], Peking University People's Hospital, Processus Infectieux en Milieu Insulaire Tropical (PIMIT), Centre National de la Recherche Scientifique (CNRS)-IRD-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de La Réunion (UR), National Perinatal Epidemiology Unit, University of Oxford [Oxford], Instituto Nacional de Enfermedades Respiratorias Dr. Emilio Coni [Santa Fe, Argentina] (INER), State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology [Wuhan] (HUST), People's Hospital of Peking University (PKUPH), Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-IRD-Centre National de la Recherche Scientifique (CNRS), University of Oxford, Muthuri, Stella G., Venkatesan, Sudhir, Myles, Puja R., Leonardi Bee, Jo, Lim, Wei Shen, Al Mamun, Abdullah, Anovadiya, Ashish P., Araújo, Wildo N., Azziz Baumgartner, Eduardo, Báez, Clarisa, Bantar, Carlo, Barhoush, Mazen M., Bassetti, Matteo, Beovic, Bojana, Bingisser, Roland, Bonmarin, Isabelle, Borja Aburto, Victor H., Cao, Bin, Carratala, Jordi, Cuezzo, María R., Denholm, Justin T., Dominguez, Samuel R., Duarte, Pericles A. D., Dubnov Raz, Gal, Echavarria, Marcela, Fanella, Sergio, Fraser, Jame, Gao, Zhancheng, Gérardin, Patrick, Giannella, Maddalena, Gubbels, Sophie, Herberg, Jethro, Higuera Iglesias, Anjarath L., Hoeger, Peter H., Hoffmann, Matthia, Xiaoyun, Hu, Islam, Quazi T., Jiménez, Mirela F., Kandeel, Amr, Keijzers, Gerben, Khalili, Hossein, Khandaker, Gulam, Knight, Marian, Kusznierz, Gabriela, Kuzman, Ilija, Kwan, Arthur M. C., Lahlou Amine, Idri, Langenegger, Eduard, Lankarani, Kamran B., Leo, Yee Sin, Linko, Rita, Liu, Pei, Madanat, Fari, Manabe, Toshie, Mayo Montero, Elga, Mcgeer, Allison, Memish, Ziad A., Metan, Gokhan, Mikić, Dragan, Mohn, Kristin G. I., Moradi, Ahmadreza, Nymadawa, Pagbajabyn, Ozbay, Bulent, Ozkan, Mehpare, Parekh, Dhruv, Paul, Mical, Poeppl, Wolfgang, Polack, Fernando P., Rath, Barbara A., Rodríguez, Alejandro H., Siqueira, Marilda M., Skrȩt Magierło, Joanna, Talarek, Ewa, Tang, Julian W., Torres, Antoni, Törün, Selda H., Tran, Dat, Uyeki, Timothy M., van Zwol, Annelie, Vaudry, Wendy, Velyvyte, Daiva, Vidmar, Tjasa, Zarogoulidis, Paul, Nguyen Van Tam, Jonathan S, de Lourdes Aguiar Oliveira, Maria, Al Khuwaitir, Tarig S. A., Al Masri, Malakita, Amin, Robed, Ballester Orcal, Elena, Bao, Jing, Basher, Ariful, Bautista, Edgar, Bertisch, Barbara, Bettinger, Julie, Booy, Robert, Bouza, Emilio, Bozkurt, Ilkay, Burgmann, Heinz, Čeljuska Tošev, Elvira, Chan, Kenny K. C., Chen, Yusheng, Chinbayar, Tserendorj, Cilloniz, Catia, Cox, Rebecca J., Sarrouf, Elena B., Cui, Wei, Dashti Khavidaki, Simin, Bin, Du, El Rhaffouli, Hicham, Escobar, Hernan, Florek Michalska, Agnieszka, Gerrard, John, Gormley, Stuart, Götberg, Sandra, Honarvar, Behnam, Jianming, Hu, Kemen, Christoph, Koay, Evelyn S. C., Kojic, Miroslav, Kudo, Koichiro, Kyaw, Win M., Leibovici, Leonard, Xiao li, Li, Hongru, Li, Libster, Romina, Loh, Tze P., Macbeth, Deborough, Maltezos, Efstratio, Marcone, Debora N., Marczynska, Magdalena, Mastalir, Fabiane P., Mickiene, Aukse, Moghadami, Mohsen, Moriconi, Lilian, Oliva, Maria E., Pečavar, Blaž, Poliquin, Philippe G., Rahman, Mahmudur, Rascon Pacheco, Alberto, Refaey, Samir, Schweiger, Brunhilde, Seale, Anna C., Sertogullarindan, Bunyamin, Smith, Fang G., Somer, Ayper, Souza, Thiago M. L., Stephan, Frank, Tabarsi, Payam, Tripathi, C. B., Viasus, Diego, Qin, Yu, Zhang, Wei, Zuo, Wei, Pediatric surgery, and ICaR - Circulation and metabolism

Subjects
Hospitalisation, Individual participant data meta-analyses, Influenza-related pneumonia, Neuraminidase inhibitors, Adolescent, Adrenal Cortex Hormones, Adult, Anti-Bacterial Agents, Antiviral Agents, Child, Child, Preschool, Enzyme Inhibitors, Female, Humans, Influenza, Human, Male, Middle Aged, Neuraminidase, Odds Ratio, Pneumonia, Viral, Treatment Outcome, Young Adult, Influenza A Virus, H1N1 Subtype, 0301 basic medicine, Epidemiology, [SDV]Life Sciences [q-bio], viruses, Meta-análises de dados de participantes individuais, Antibiotics, Pneumònia, Adrenal Cortex Hormone, 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit, influenza-related pneumonia, neuraminidase inhibitors, 0302 clinical medicine, individual participant data meta‐analyses, Influenza A Virus, Enzyme Inhibitor, 030212 general & internal medicine, Viral, Incidence (epidemiology), Inibidores da neuraminidase, virus diseases, 3. Good health, Hospitalization, Infectious Diseases, Meta-analysis, Original Article, Individual participant data meta-analyse, Public Health, Human, Pulmonary and Respiratory Medicine, medicine.medical_specialty, Antagonists & inhibitors, medicine.drug_class, influenza-related pneumonia, 030106 microbiology, influenza‐related pneumonia, Neuraminidase inhibitor, Ingressos i altes en els hospitals, Public Health, Environmental and Occupational Health, 03 medical and health sciences, Open Access, Pneumonia relacionada à gripe, Pharmacotherapy, Internal medicine, Anti-Bacterial Agent, medicine, H1N1 Subtype, Preschool, Antiviral Agent, Hospitalização, Hospital admission and discharge, business.industry, Environmental and Occupational Health, individual participant data meta-analyses, Original Articles, Odds ratio, Pneumonia, medicine.disease, Influenza, respiratory tract diseases, El Niño, Immunology, business
Abstract

Stella G. Muthuri,1 Sudhir Venkatesan,1 Puja R. Myles,1 Jo Leonardi-Bee,1 Wei Shen Lim,2 Abdullah Al Mamun,3 Ashish P. Anovadiya,4 Wildo N. Ara ujo,5 Eduardo Azziz-Baumgartner,6 Clarisa B aez,7 Carlos Bantar,8 Mazen M. Barhoush,9 Matteo Bassetti,10 Bojana Beovic,11 Roland Bingisser,12 Isabelle Bonmarin,13 Victor H. Borja-Aburto,14 Bin Cao,15 Jordi Carratala,16 Mar ıa R. Cuezzo,17 Justin T. Denholm,18 Samuel R. Dominguez,19 Pericles A. D. Duarte,20 Gal Dubnov-Raz,21 Marcela Echavarria,22 Sergio Fanella,23 James Fraser,24 Zhancheng Gao,25 Patrick G erardin,26,27,28,29 Maddalena Giannella,30 Sophie Gubbels,31 Jethro Herberg,32 Anjarath L. Higuera Iglesias,33 Peter H. Hoeger,34 Matthias Hoffmann,35 Xiaoyun Hu,36 Quazi T. Islam,37 Mirela F. Jim enez,38 Amr Kandeel,39 Gerben Keijzers,40 Hossein Khalili,41 Gulam Khandaker,42 Marian Knight,43 Gabriela Kusznierz,44 Ilija Kuzman,45 Arthur M. C. Kwan,46 Idriss Lahlou Amine,47 Eduard Langenegger,48 Kamran B. Lankarani,49 Yee-Sin Leo,50 Rita Linko,51 Pei Liu,52 Faris Madanat,53 Toshie Manabe,54 Elga Mayo-Montero,55 Allison McGeer,56 Ziad A. Memish,57,58 Gokhan Metan,59 Dragan Miki c,60 Kristin G. I. Mohn,61,62 Ahmadreza Moradi,63,64 Pagbajabyn Nymadawa,65 Bulent Ozbay,66 Mehpare Ozkan,67 Dhruv Parekh,68 Mical Paul,69 Wolfgang Poeppl,70 Fernando P. Polack,71,72 Barbara A. Rath,73 Alejandro H. Rodr ıguez,74 Marilda M. Siqueira,75 Joanna Skre zt-Magierło,76 Ewa Talarek,77 Julian W. Tang,78,79,80 Antoni Torres,81 Selda H. T€ or€un,82 Dat Tran,83 Timothy M. Uyeki,84 Annelies van Zwol,85 Wendy Vaudry,86 Daiva Velyvyte,87 Tjasa Vidmar,88 Paul Zarogoulidis,89 PRIDE Consortium Investigators* Jonathan S. Nguyen-Van-Tam1 1Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK. 2Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham, UK. 3International Centre for Diarrhoeal Diseases, Research Bangladesh (ICDDRB), Dhaka, Bangladesh. 4Department of Pharmacology, Government Medical College and Sir Takhtsinhji General Hospital, Bhavnagar, Gujarat, India. 5University of Bras ılia, Bras ılia, DF, Brazil. 6Centers for Disease Control and Prevention, Atlanta, GA, USA. 7Ministerio de Salud de la Provincia de Buenos Aires, Buenos Aires, Argentina. 8Department of Infection Control, Hospital San Mart ın de Paran a, Entre R ıos, Argentina. 9Department of Medicine, King Saud Medical City, Riyadh, Saudi Arabia. 10Santa Maria Misericordia Hospital, Udine, Italy. 11Department of Infectious Diseases, University Medical Centre, Ljubljana, Slovenia. 12Department of Emergency Medicine, University Hospital Basel, Basel, Switzerland. 13Institut de Veille Sanitaire, Saint-Maurice, France. 14Instituto Mexicano del Seguro Social (IMSS), Mexico City, Mexico. 15Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. 16Department of Infectious Diseases, Hospital Universitari de Bellvitge, Bellvitge Institute for Biomedical Research, L’Hospitalet de Llobregat, Red Espa~nola de Investigaci on en Patolog ıa Infecciosa, University of Barcelona, Barcelona, Spain. 17Ministerio de Salud de Tucum an, Tucum an, Argentina. 18Victorian Infectious Diseases Service and Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, Parkville, Vic., Australia. 19Department of Pediatric Infectious Diseases, Children’s Hospital Colorado, University of Colorado School of Medicine, Aurora, CO, USA. 20Universidade Estadual do Oeste do Parana ´, UNIOESTE, Cascavel, PR, Brazil. 21The Edmond and Lily Safra Children’s Hospital, Sheba Medical Center, Tel-Hashomer, Israel. 22Clinical Virology Laboratory, CEMIC University Hospital, Buenos Aires, Argentina. 23Section of Pediatric Infectious Diseases, University of Manitoba, Winnipeg, MB, Canada. 24Paediatric Intensive Care Unit, Bristol Children’s Hospital, Bristol, UK. 25Department of Respiratory & Critical Care Medicine, Peking University People’s Hospital, Beijing, China. 26NICU/PICU, PFME, CHU Saint Pierre, Saint Pierre, La R eunion, France. 27CIC 1410 (CHU/Inserm/University of La Re ´union/URML-OI), CHU Saint Pierre, Saint Pierre, La Réunion, France. 28UMR PIMIT (CHU/Inserm/University of La Re ´union/IRD/CNRS), CYROI, Saint Denis – Reunion Island, Saint Denis, France. 29NICU/PICU CHU of La Re ´union, Groupe Hospitalier Sud Re ´union, Saint Pierre, La Re ´union, France. 30Department of Clinical Microbiology and Infectious Diseases, Hospital General Universitario Gregorio Maran ˜o ´n, Madrid, Spain. 31Department of Infectious Disease Epidemiology, Sector for National Health Documentation and Research, Statens Serum Institut, Copenhagen, Denmark. 32Section of Paediatrics, Division of Infectious Disease, Imperial College, London, UK. 33Epidemiology Research Unit, Instituto Nacional de Enfermedades Respiratorias, Ismael Cosı ´o Villegas, Mexico City, Mexico. 34Cath. Children’s Hospital Wilhelmstift, Hamburg, Germany. 35Division of Infectious Diseases and Hospital Epidemiology, Kantonsspital St. Gallen, St. Gallen, Switzerland. 36Peking Union Medical College Hospital, Beijing, China. 37Dhaka Medical College Hospital, Dhaka, Bangladesh. 38Departamento de Ginecologia e Obstetrı ´cia – UFCSPA, Preceptora da Reside ˆncia Me ´dica do Hospital Fe ˆmina, Porto Alegre, Brazil. 39Ministry of Health in Egypt, Cairo, Egypt. 40Gold Coast Hospital, Gold Coast, Qld, Australia. 41Department of Clinical Pharmacy, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran. 42National Centre for Immunisation Research and Surveillance (NCIRS), The Children’s Hospital at Westmead, University of Sydney, Sydney, NSW, Australia. 43National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK. 44National Institute of Respiratory Diseases ‘Emilio Coni’ ANLIS “C. Malbran”, Santa Fe, Argentina. 45School of Medicine, University Hospital for Infectious Diseases, University of Zagreb, Zagreb, Croatia. 46Department of Intensive Care, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong. 47Faculty of Medicine and Pharmacy, Mohammed V Military Teaching Hospital, Biosafety Level 3 and Research Laboratory, University Mohammed V-Souissi, Rabat, Morocco. 48Department of Obstetrics and Gynaecology, Stellenbosch University and Tygerberg, Stellenbosch, South Africa. 49Health Policy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. 50Department of Infectious Diseases, Tan Tock Seng Hospital, Singapore, Singapore. 51Helsinki University Hospital, Helsinki, Finland. 52Department of Infectious Diseases, The First Affiliated Hospital, China Medical University, Shenyang, China. 53Department of Pediatrics, King Hussein Cancer Center, Amman, Jordan. 54Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan. 55Instituto de Medicina Preventiva de la Defensa, Capitan Medico Ramon y Cajal (IMPDEF), Ministerio de Defensa, Madrid, Spain. 56Toronto Invasive Bacterial Diseases Network, University of Toronto, Toronto, ON, Canada. 57Ministry of Health, Riyadh, Saudi Arabia. 58College of Medicine, Alfaisal University, Riyadh, Saudi Arabia. 59Department of Infectious Diseases and Clinical Microbiology, Erciyes University Faculty of Medicine, Kayseri, Turkey. 60Military Medical Academy, Clinic for Infectious and Tropical Diseases, Belgrade, Serbia. 61Section for Infectious Diseases, Medical Department, and Department of Research and Development, Haukeland University Hospital, Bergen, Norway. 62Department of Clinical Science, The Influenza Centre, University of Bergen, Bergen, Norway. 63The Division of Ocular Immunology, Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 64National Research Institute for Tuberculosis and Lung Disease, Massih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 65National Influenza Center, National Center of Communicable Diseases, Ministry of Health, Ulaanbaatar, Mongolia. 66Department of Pulmonary and Critical Care, Yuzuncu Yil University Medical Faculty, Van, Turkey. 67Clinic of Pediatric Neurology, Dr. Sami Ulus Research and Training Hospital of Women’s and Children’s Health and Diseases, Ankara, Turkey. 68Critical Care and Pain Perioperative, Critical Care and Trauma Trials Group, School of Clinical and Experimental Medicine, University of Birmingham, Birmingham, UK. 69Division of Infectious Diseases, Rambam Health Care Campus, Haifa, Israel. 70Medical University of Vienna, Vienna, Austria. 71Department of Pediatrics, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA. 72Fundacion INFANT, Buenos Aires, Argentina. 73Division of Pneumonology-Immunology, Department of Pediatrics, Charite ´ University Medical Center, Berlin, Germany. 74Critical Care Department, Hospital Joan XXIII, IISPV, URV, CIBERES, Tarragona, Spain. 75Laboratory of Respiratory Viruses, Oswaldo Cruz Institute/Fiocruz, Rio de Janeiro, Brazil. 76Uniwersytet Rzeszowski, Rzeszo ´w, Poland. 77Department of Children’s Infectious Diseases, Medical University of Warsaw, Warsaw, Poland. 78Division of Microbiology/Molecular Diagnostic Centre, Department of Laboratory Medicine, National University Hospital, Singapore, Singapore. 79Alberta Provincial Laboratory for Public Health, University of Alberta Hospital, Edmonton, Canada. 80Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB, Canada. 81Hospital Clinic, University of Barcelona, IDIBAPS, CIBERES, Barcelona, Spain. 82Department of Pediatric Infectious Diseases, Istanbul Medical Faculty, Istanbul, Turkey. 83Division of Infectious Diseases, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Canada. 84Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA. 85Department of Pediatric Intensive Care, VU University Medical Center, Amsterdam, The Netherlands. 86Division of Infectious Diseases, Department of Pediatrics, Stollery Children’s Hospital, University of Alberta, Edmonton, AB, Canada. 87Lithuanian University of Health Sciences, Kaunas, Lithuania. 88General Hospital, Slovenj Gradec, Slovenia. 89Unit of Infectious Diseases, University General Hospital of Alexandroupolis, Democritus University Thrace, Dragana, Greece. Correspondence: Jonathan S. Nguyen-Van-Tam, University of Nottingham, City Hospital, DM, Room A28b, Clinical Sciences Building, Nottingham NG5 1PB, UK. E-mail: jvt@nottingham.ac.uk *List of PRIDE Consortium Investigators are in Appendix 1. For affiliations, please see Table S1. Múltipla - ver em notas Background The impact of neuraminidase inhibitors (NAIs) on influenza-related pneumonia (IRP) is not established. Our objective was to investigate the association between NAI treatment and IRP incidence and outcomes in patients hospitalised with A(H1N1) pdm09 virus infection. Methods A worldwide meta-analysis of individual participant data from 20 634 hospitalised patients with laboratory-confirmed A (H1N1)pdm09 (n = 20 021) or clinically diagnosed (n = 613) ‘pandemic influenza’. The primary outcome was radiologically confirmed IRP. Odds ratios (OR) were estimated using generalised linear mixed modelling, adjusting for NAI treatment propensity, antibiotics and corticosteroids. Results Of 20 634 included participants, 5978 (29 0%) had IRP; conversely, 3349 (16 2%) had confirmed the absence of radiographic pneumonia (the comparator). Early NAI treatment (within 2 days of symptom onset) versus no NAI was not significantly associated with IRP [adj. OR 0 83 (95% CI 0 64–1 06; P = 0 136)]. Among the 5978 patients with IRP, early NAI treatment versus none did not impact on mortality [adj. OR = 0 72 (0 44–1 17; P = 0 180)] or likelihood of requiring ventilatory support [adj. OR = 1 17 (0 71– 1 92; P = 0 537)], but early treatment versus later significantly reduced mortality [adj. OR = 0 70 (0 55–0 88; P = 0 003)] and likelihood of requiring ventilatory support [adj. OR = 0 68 (0 54– 0 85; P = 0 001)]. Conclusions Early NAI treatment of patients hospitalised with A (H1N1)pdm09 virus infection versus no treatment did not reduce the likelihood of IRP. However, in patients who developed IRP, early NAI treatment versus later reduced the likelihood of mortality and needing ventilatory support.

Published
2016

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