scholarly journals Prevalence of COVID-19-related risk factors and risk of severe influenza outcomes in cancer survivors: A matched cohort study using linked English electronic health records data

2020 ◽  
Vol 29-30 ◽  
pp. 100656
Author(s):  
Helena Carreira ◽  
Helen Strongman ◽  
Maria Peppa ◽  
Helen I. McDonald ◽  
Isabel dos-Santos-Silva ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Electronic Health Records ◽  
Cancer Survivors ◽  
Matched Cohort ◽  
Health Records ◽  
Matched Cohort Study ◽  
Severe Influenza ◽  
Related Risk ◽  
Electronic Health

scholarly journals Prevalence of COVID-19-related risk factors and risk of severe influenza outcomes in cancer survivors: a matched cohort study using linked English electronic health records data

2020 ◽  
Author(s):  
Helena Carreira ◽  
Helen Strongman ◽  
Maria Peppa ◽  
Helen I McDonald ◽  
Isabel dos-Santos-Silva ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Electronic Health Records ◽  
Cancer Survivors ◽  
Cancer Diagnosis ◽  
Risk Groups ◽  
Matched Cohort ◽  
Health Records ◽  
Severe Influenza ◽  
Clinical Risk Groups

AbstractBackgroundPeople with active cancer are recognised as at risk of COVID-19 complications, but it is unclear whether the much larger population of cancer survivors is at elevated risk. We aimed to address this by comparing cancer survivors and cancer-free controls for (i) prevalence of comorbidities considered risk factors for COVID-19; and (ii) risk of severe influenza, as a marker of susceptibility to severe outcomes from epidemic respiratory viruses.MethodsWe included survivors (≥1 year) of the 20 most common cancers, and age, sex and general practice-matched cancer-free controls, derived from UK primary care data linked to cancer registrations, hospital admissions and death registrations. Comorbidity prevalences were calculated 1 and 5 years from cancer diagnosis. Risk of hospitalisation or death due to influenza was compared using Cox models adjusted for baseline demographics and comorbidities.Findings108,215 cancer survivors and 523,541 cancer-free controls were included. Cancer survivors had more asthma, other respiratory, cardiac, diabetes, neurological, renal, and liver disease, and less obesity, compared with controls, but there was variation by cancer site. There were 205 influenza hospitalisations/deaths, with cancer survivors at higher risk than controls (adjusted HR 2.78, 95% CI 2.04-3.80). Haematological cancer survivors had large elevated risks persisting for >10 years (HR overall 15.17, 7.84-29.35; HR >10 years from cancer diagnosis 10.06, 2.47-40.93). Survivors of other cancers had evidence of raised risk up to 5 years from cancer diagnosis only (HR 2.22, 1.31-3.74).InterpretationRisks of severe COVID-19 outcomes are likely to be elevated in cancer survivors. This should be taken into account in policies targeted at clinical risk groups, and vaccination for both influenza, and, when available, COVID-19, should be encouraged in cancer survivors.FundingWellcome Trust, Royal Society, NIHR.Research in contextEvidence before this studyFew data are available to date on how COVID-19 affects cancer survivors. We searched PubMed with the keywords “influenza cancer survivors” to identify studies that compared severe influenza outcomes in cancer survivors and in a control group. No study was identified.Added value of this studyIn this matched cohort study of routinely collected electronic health records, we demonstrated raised risks of influenza hospitalisation or mortality in survivors from haematological malignancies for >10 years after diagnosis, and in survivors from solid cancers up to 5 years after diagnosis.Implications of all the available evidenceCancer survivorship appears to be an important risk factor for severe influenza outcomes, suggesting that cancer survivors may also be at raised risk of poor COVID-19 outcomes. This should be taken into account in public health policies targeted at protecting clinical risk groups. Influenza vaccination should be encouraged in this group, and may need to be extended to a wider population of medium- to long-term cancer survivors than currently recommended.

scholarly journals Do health checks improve risk factor detection in primary care? Matched cohort study using electronic health records

2015 ◽  
Vol 38 (3) ◽  
pp. 552-559 ◽  
Author(s):  
Alice S. Forster ◽  
Caroline Burgess ◽  
Hiten Dodhia ◽  
Frances Fuller ◽  
Jane Miller ◽  
...  
Keyword(s):  
Primary Care ◽  
Risk Factor ◽  
Cohort Study ◽  
Electronic Health Records ◽  
Matched Cohort ◽  
Health Records ◽  
Matched Cohort Study ◽  
Health Checks ◽  
Electronic Health

scholarly journals MORTALITY OF CARE HOME RESIDENTS AND COMMUNITY-DWELLING CONTROLS DURING THE COVID-19 PANDEMIC IN 2020: MATCHED COHORT STUDY

2021 ◽  
Author(s):  
Martin C Gulliford ◽  
Emma C Rezel-Potts
Keyword(s):  
Primary Care ◽  
Cohort Study ◽  
Electronic Health Records ◽  
Care Home ◽  
Community Dwelling ◽  
Matched Cohort ◽  
Long Term Conditions ◽  
Health Records ◽  
Matched Cohort Study ◽  
Electronic Health

Objective: To estimate mortality of care home residents during the Covid-19 pandemic from primary care electronic health records. Design: Matched cohort study Setting: 1,421 general practices contributing to the Clinical Practice Research Datalink Aurum Database in England. Participants: 217,987 patients aged 18 to 104 years with recorded care home residence in England in the period 2015 to 2020. There were 86,371 care home residents contributing data in 2020, with 29,662 deaths; 83,419 (97%) were matched on age, gender and general practice with 312,607 community-dwelling adults. Main outcome measures: All-cause mortality. Analysis was by Poisson regression adjusting for age, gender, long-term conditions, region, year and calendar week. Results: The highest first wave age-specific mortality rate was 6.02 (95% confidence interval 5.97 to 6.07) per 100 patients per week in men aged 95-104 years between 13th-19th April 2020. Compared with community-dwelling controls, the adjusted rate ratio for mortality of care home residents was 4.95 (4.62 to 5.32) in February 2020, increasing to 8.34 (7.95 to 8.74) in April 2020, declining to 3.93 (3.68 to 4.20) in December 2020. During the week of 13th to 19th April 2020, mortality of care home residents was 10.74 (9.72 to 11.85) times higher than for matched community-dwelling controls. Conclusions: Individual-patient data from primary care electronic health records may be used to estimate mortality in care home residents. Mortality is substantially higher than for community-dwelling comparators and showed a disproportionate increase in the first wave of the Covid-19 pandemic but not the second wave. This study provides evidence to support earlier, decisive action to protect these vulnerable populations in the event of further outbreaks. Prospective investigations of care home mortality are warranted.

scholarly journals OpenSAFELY: factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients

2020 ◽  
Author(s):  
◽  
Elizabeth Williamson ◽  
Alex J Walker ◽  
Krishnan Bhaskaran ◽  
Seb Bacon ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Cohort Study ◽  
Electronic Health Records ◽  
Clinical Risk Factors ◽  
Hospital Death ◽  
Health Records ◽  
Clinical Risk ◽  
Hazard Ratios ◽  
Electronic Health

AbstractBackgroundEstablishing who is at risk from a novel rapidly arising cause of death, and why, requires a new approach to epidemiological research with very large datasets and timely data. Working on behalf of NHS England we therefore set out to deliver a secure and pseudonymised analytics platform inside the data centre of a major primary care electronic health records vendor establishing coverage across detailed primary care records for a substantial proportion of all patients in England. The following results are preliminary.Data sourcesPrimary care electronic health records managed by the electronic health record vendor TPP, pseudonymously linked to patient-level data from the COVID-19 Patient Notification System (CPNS) for death of hospital inpatients with confirmed COVID-19, using the new OpenSAFELY platform.Population17,425,445 adults.Time period1st Feb 2020 to 25th April 2020.Primary outcomeDeath in hospital among people with confirmed COVID-19.MethodsCohort study analysed by Cox-regression to generate hazard ratios: age and sex adjusted, and multiply adjusted for co-variates selected prospectively on the basis of clinical interest and prior findings.ResultsThere were 5683 deaths attributed to COVID-19. In summary after full adjustment, death from COVID-19 was strongly associated with: being male (hazard ratio 1.99, 95%CI 1.88-2.10); older age and deprivation (both with a strong gradient); uncontrolled diabetes (HR 2.36 95% CI 2.18-2.56); severe asthma (HR 1.25 CI 1.08-1.44); and various other prior medical conditions. Compared to people with ethnicity recorded as white, black people were at higher risk of death, with only partial attenuation in hazard ratios from the fully adjusted model (age-sex adjusted HR 2.17 95% CI 1.84-2.57; fully adjusted HR 1.71 95% CI 1.44-2.02); with similar findings for Asian people (age-sex adjusted HR 1.95 95% CI 1.73-2.18; fully adjusted HR 1.62 95% CI 1.431.82).ConclusionsWe have quantified a range of clinical risk factors for death from COVID-19, some of which were not previously well characterised, in the largest cohort study conducted by any country to date. People from Asian and black groups are at markedly increased risk of in-hospital death from COVID-19, and contrary to some prior speculation this is only partially attributable to pre-existing clinical risk factors or deprivation; further research into the drivers of this association is therefore urgently required. Deprivation is also a major risk factor with, again, little of the excess risk explained by co-morbidity or other risk factors. The findings for clinical risk factors are concordant with policies in the UK for protecting those at highest risk. Our OpenSAFELY platform is rapidly adding further NHS patients’ records; we will update and extend these results regularly.

scholarly journals Predicting dementia diagnosis from cognitive footprints in electronic health records: a case–control study protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e043487
Author(s):  
Hao Luo ◽  
Kui Kai Lau ◽  
Gloria H Y Wong ◽  
Wai-Chi Chan ◽  
Henry K F Mak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Hong Kong ◽  
Electronic Health Records ◽  
Case Control Study ◽  
Case Control ◽  
Dementia Diagnosis ◽  
Health Records ◽  
Electronic Health ◽  
Control Study

IntroductionDementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case–control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history.Methods and analysisWe will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared.Ethics and disseminationThis study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients’ records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities’ Action in Response to Dementia project (https://www.tip-card.hku.hk/).

scholarly journals 1665. Using Electronic Health Records to Describe TB in Community Health Settings: a Cohort Analysis in a Large Safety-Net Population

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S819-S820
Author(s):  
Jonathan Todd ◽  
Jon Puro ◽  
Matthew Jones ◽  
Jee Oakley ◽  
Laura A Vonnahme ◽  
...  
Keyword(s):  
United States ◽  
Risk Factors ◽  
Electronic Health Records ◽  
Safety Net ◽  
The United States ◽  
Research Network ◽  
Health Records ◽  
Treatment Regimens ◽  
Data Source ◽  
Electronic Health

Abstract Background Over 80% of tuberculosis (TB) cases in the United States are attributed to reactivation of latent TB infection (LTBI). Eliminating TB in the United States requires expanding identification and treatment of LTBI. Centralized electronic health records (EHRs) are an unexplored data source to identify persons with LTBI. We explored EHR data to evaluate TB and LTBI screening and diagnoses within OCHIN, Inc., a U.S. practice-based research network with a high proportion of Federally Qualified Health Centers. Methods From the EHRs of patients who had an encounter at an OCHIN member clinic between January 1, 2012 and December 31, 2016, we extracted demographic variables, TB risk factors, TB screening tests, International Classification of Diseases (ICD) 9 and 10 codes, and treatment regimens. Based on test results, ICD codes, and treatment regimens, we developed a novel algorithm to classify patient records into LTBI categories: definite, probable or possible. We used multivariable logistic regression, with a referent group of all cohort patients not classified as having LTBI or TB, to identify associations between TB risk factors and LTBI. Results Among 2,190,686 patients, 6.9% (n=151,195) had a TB screening test; among those, 8% tested positive. Non-U.S. –born or non-English–speaking persons comprised 24% of our cohort; 11% were tested for TB infection, and 14% had a positive test. Risk factors in the multivariable model significantly associated with being classified as having LTBI included preferring non-English language (adjusted odds ratio [aOR] 4.20, 95% confidence interval [CI] 4.09–4.32); non-Hispanic Asian (aOR 5.17, 95% CI 4.94–5.40), non-Hispanic black (aOR 3.02, 95% CI 2.91–3.13), or Native Hawaiian/other Pacific Islander (aOR 3.35, 95% CI 2.92–3.84) race; and HIV infection (aOR 3.09, 95% CI 2.84–3.35). Conclusion This study demonstrates the utility of EHR data for understanding TB screening practices and as an important data source that can be used to enhance public health surveillance of LTBI prevalence. Increasing screening among high-risk populations remains an important step toward eliminating TB in the United States. These results underscore the importance of offering TB screening in non-U.S.–born populations. Disclosures All Authors: No reported disclosures

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