Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 40
Filter
1.
Thromb Res ; 212: 44-50, 2022 04.
Article in English | MEDLINE | ID: covidwho-1699972

ABSTRACT

BACKGROUND: Pulmonary embolism is a known complication of coronavirus disease 2019 (COVID-19). Epidemiological population data focusing on pulmonary embolism-related mortality is limited. METHODS: Veneto is a region in Northern Italy counting 4,879,133 inhabitants in 2020. All ICD-10 codes from death certificates (1st January 2018 to 31st December 2020) were examined. Comparisons were made between 2020 (COVID-19 outbreak) and the average of the two-year period 2018-2019. All-cause, COVID-19-related and the following cardiovascular deaths have been studied: pulmonary embolism, hypertensive disease, ischemic heart disease, atrial fibrillation/flutter, and cerebrovascular diseases. RESULTS: In 2020, a total of 56,412 deaths were recorded, corresponding to a 16% (n = 7806) increase compared to the period 2018-2019. The relative percentage increase during the so-called first and second waves was 19% and 44%, respectively. Of 7806 excess deaths, COVID-19 codes were reported in 90% of death certificates. The percentage increase in pulmonary embolism-related deaths was 27% (95%CI 19-35%), 1018 deaths during the year 2020, compared to 804 mean annual deaths in the period 2018-2019. This was more evident among men, who experience an absolute increase of 147 deaths (+45%), than in women (+67 deaths; +14%). The increase was primarily driven by deaths recorded during the second wave (+91% in October-December). An excess of deaths, particularly among men and during the second wave, was also observed for other cardiovascular diseases, notably hypertensive disease, atrial fibrillation, cerebrovascular disease, and ischemic heart disease. CONCLUSIONS: We observed a considerable increase of all-cause mortality during the year 2020. This was mainly driven by COVID-19 and its complications. The relative increase in the number of pulmonary embolism-related deaths was more prominent during the second wave, suggesting a possible underdiagnosis during the first wave.


Subject(s)
COVID-19 , Pulmonary Embolism , COVID-19/complications , Female , Humans , International Classification of Diseases , Italy/epidemiology , Male , Pandemics , Pulmonary Embolism/epidemiology
2.
PLoS One ; 17(2): e0260150, 2022.
Article in English | MEDLINE | ID: covidwho-1690761

ABSTRACT

BACKGROUND: The French syndromic surveillance (SyS) system, SurSaUD®, was one of the systems used to monitor the COVID-19 outbreak. AIM: This study described the epidemiological characteristics of COVID-19-related visits to both emergency departments (EDs) and the network of emergency general practitioners known as SOS Médecins (SOSMed) in France from 17 February to 28 June 2020. METHODS: Data on all visits to 634 EDs and 60 SOSMed associations were collected daily. COVID-19-related visits were identified using ICD-10 codes after coding recommendations were sent to all ED and SOSMed doctors. The time course of COVID-19-related visits was described by age group and region. During the lockdown period, the characteristics of ED and SOSMed visits and hospitalisations after visits were described by age group and gender. The most frequent diagnoses associated with COVID-19-related visits were analysed. RESULTS: COVID-19 SyS was implemented on 29 February and 4 March for EDs and SOSMed, respectively. A total of 170,113 ED and 59,087 SOSMed visits relating to COVID-19 were recorded, representing 4.0% and 5.6% of the overall coded activity with a peak in late March representing 22.5% and 25% of all ED and SOSMed visits, respectively. COVID-19-related visits were most frequently reported for women and those aged 15-64 years, although patients who were subsequently hospitalised were more often men and persons aged 65 years and older. CONCLUSION: SyS allowed for population health monitoring of the COVID-19 epidemic in France. As SyS has more than 15 years of historical data with high quality and reliability, it was considered sufficiently robust to contribute to defining the post-lockdown strategy.


Subject(s)
COVID-19/epidemiology , Disease Outbreaks , Population Health , Seasons , Sentinel Surveillance , COVID-19/diagnosis , Emergency Service, Hospital , France/epidemiology , Geography , Humans , International Classification of Diseases
3.
BMJ ; 376: e068414, 2022 02 09.
Article in English | MEDLINE | ID: covidwho-1677376

ABSTRACT

OBJECTIVE: To characterize the risk of persistent and new clinical sequelae in adults aged ≥65 years after the acute phase of SARS-CoV-2 infection. DESIGN: Retrospective cohort study. SETTING: UnitedHealth Group Clinical Research Database: deidentified administrative claims and outpatient laboratory test results. PARTICIPANTS: Individuals aged ≥65 years who were continuously enrolled in a Medicare Advantage plan with coverage of prescription drugs from January 2019 to the date of diagnosis of SARS-CoV-2 infection, matched by propensity score to three comparison groups that did not have covid-19: 2020 comparison group (n=87 337), historical 2019 comparison group (n=88 070), and historical comparison group with viral lower respiratory tract illness (n=73 490). MAIN OUTCOME MEASURES: The presence of persistent and new sequelae at 21 or more days after a diagnosis of covid-19 was determined with ICD-10 (international classification of diseases, 10th revision) codes. Excess risk for sequelae caused by infection with SARS-CoV-2 was estimated for the 120 days after the acute phase of the illness with risk difference and hazard ratios, calculated with 95% Bonferroni corrected confidence intervals. The incidence of sequelae after the acute infection was analyzed by age, race, sex, and whether patients were admitted to hospital for covid-19. RESULTS: Among individuals who were diagnosed with SARS-CoV-2, 32% (27 698 of 87 337) sought medical attention in the post-acute period for one or more new or persistent clinical sequelae, which was 11% higher than the 2020 comparison group. Respiratory failure (risk difference 7.55, 95% confidence interval 7.18 to 8.01), fatigue (5.66, 5.03 to 6.27), hypertension (4.43, 2.27 to 6.37), memory difficulties (2.63, 2.23 to 3.13), kidney injury (2.59, 2.03 to 3.12), mental health diagnoses (2.50, 2.04 to 3.04), hypercoagulability 1.47 (1.2 to 1.73), and cardiac rhythm disorders (2.19, 1.76 to 2.57) had the greatest risk differences compared with the 2020 comparison group, with similar findings to the 2019 comparison group. Compared with the group with viral lower respiratory tract illness, however, only respiratory failure, dementia, and post-viral fatigue had increased risk differences of 2.39 (95% confidence interval 1.79 to 2.94), 0.71 (0.3 to 1.08), and 0.18 (0.11 to 0.26) per 100 patients, respectively. Individuals with severe covid-19 disease requiring admission to hospital had a markedly increased risk for most but not all clinical sequelae. CONCLUSIONS: The results confirm an excess risk for persistent and new sequelae in adults aged ≥65 years after acute infection with SARS-CoV-2. Other than respiratory failure, dementia, and post-viral fatigue, the sequelae resembled those of viral lower respiratory tract illness in older adults. These findings further highlight the wide range of important sequelae after acute infection with the SARS-CoV-2 virus.


Subject(s)
COVID-19/complications , Aged , COVID-19/diagnosis , COVID-19/epidemiology , Chronic Disease/epidemiology , Cohort Studies , Female , Humans , Incidence , International Classification of Diseases , Male , Medicare Part C , Patient Acuity , Propensity Score , Retrospective Studies , Risk , United States/epidemiology
4.
BMJ Open ; 12(1): e057838, 2022 01 21.
Article in English | MEDLINE | ID: covidwho-1642872

ABSTRACT

OBJECTIVE: To evaluate the validity of COVID-19 International Classification of Diseases, 10th Revision (ICD-10) codes and their combinations. DESIGN: Retrospective cohort study. SETTING: Acute care hospitals and emergency departments (EDs) in Alberta, Canada. PARTICIPANTS: Patients who were admitted to hospital or presented to an ED in Alberta, as captured by local administrative databases between 1 March 2020 and 28 February 2021, who had a positive COVID-19 test and/or a COVID-19-related ICD-10 code. MAIN OUTCOME MEASURES: The sensitivity, positive predictive value (PPV) and 95% CIs for ICD-10 codes were computed. Stratified analysis on age group, sex, symptomatic status, mechanical ventilation, hospital type, patient intensive care unit (ICU) admission, discharge status and season of pandemic were conducted. RESULTS: Two overlapping subsets of the study population were considered: those who had a positive COVID-19 test (cohort A, for estimating sensitivity) and those who had a COVID-19-related ICD-10 code (cohort B, for estimating PPV). Cohort A included 17 979 ED patients and 6477 inpatients while cohort B included 33 675 ED patients and 18 746 inpatients. Of inpatients, 9.5% in cohort A and 8.1% in cohort B received mechanical ventilation. Over 13% of inpatients were admitted to ICU. The length of hospital stay was 6 days (IQR: 3-14) for cohort A and 8 days (IQR: 3-19) for cohort B. In-hospital mortality was 15.9% and 38.8% for cohort A and B, respectively. The sensitivity for ICD-10 code U07.1 (COVID-19, virus identified) was 82.5% (81.8%-83.2%) with a PPV of 93.1% (92.6%-93.6%). The combination of U07.1 and U07.3 (multisystem inflammatory syndrome associated with COVID-19) had a sensitivity of 82.5% (81.9%-83.2%) and PPV of 92.9% (92.4%-93.4%). CONCLUSIONS: In Alberta, ICD-10 COVID-19 codes (U07.1 and U07.3) were coded well with high validity. This indicates administrative data can be used for COVID-19 research and pandemic management purposes.


Subject(s)
COVID-19 , International Classification of Diseases , Alberta/epidemiology , Cohort Studies , Hospitals , Humans , Retrospective Studies , SARS-CoV-2
5.
Sci Rep ; 12(1): 328, 2022 01 10.
Article in English | MEDLINE | ID: covidwho-1616999

ABSTRACT

Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study has aimed to cope with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the decision rules derived from the machine learning based prediction models can provide valuable guidelines for the healthcare policy makers to develop an effective vaccination strategy. Furthermore, in case the healthcare facilities are overwhelmed by patients with EID, which frequently occurred in the recent COVID-19 pandemic, the frontline physicians can incorporate the proposed prediction models to triage patients suffering minor symptoms without laboratory tests, which may become scarce during an EID disaster. In conclusion, our study has demonstrated an effective approach to exploit machine learning technologies to cope with the challenges faced during the outbreak of an EID.


Subject(s)
COVID-19/epidemiology , Communicable Diseases, Emerging/epidemiology , Hospitalization/statistics & numerical data , Machine Learning , Preventive Medicine/statistics & numerical data , Public Health/statistics & numerical data , COVID-19/prevention & control , COVID-19/virology , Communicable Diseases, Emerging/prevention & control , Hospital Mortality , Humans , International Classification of Diseases , Logistic Models , Models, Theoretical , Pandemics/prevention & control , Preventive Medicine/methods , Public Health/methods , Risk Factors , SARS-CoV-2/physiology , Severity of Illness Index
6.
Int J Environ Res Public Health ; 19(1)2022 Jan 05.
Article in English | MEDLINE | ID: covidwho-1613776

ABSTRACT

The Australian mortality data are a foundational health dataset which supports research, policy and planning. The COVID-19 pandemic necessitated the need for more timely mortality data that could assist in monitoring direct mortality from the virus as well as indirect mortality due to social and economic societal change. This paper discusses the evolution of mortality data in Australia during the pandemic and looks at emerging opportunities associated with electronic infrastructure such as electronic Medical Certificates of Cause of Death (eMCCDs), ICD-11 and automated coding tools that will form the foundations of a more responsive and comprehensive future mortality dataset.


Subject(s)
COVID-19 , Pandemics , Australia/epidemiology , Humans , International Classification of Diseases , SARS-CoV-2
7.
Pharmacoepidemiol Drug Saf ; 31(4): 476-480, 2022 04.
Article in English | MEDLINE | ID: covidwho-1574764

ABSTRACT

PURPOSE: Health plan claims may provide complete longitudinal data for timely, real-world population-level COVID-19 assessment. However, these data often lack laboratory results, the standard for COVID-19 diagnosis. METHODS: We assessed the validity of ICD-10-CM diagnosis codes for identifying patients hospitalized with COVID-19 in U.S. claims databases, compared to linked laboratory results, among six Food and Drug Administration Sentinel System data partners (two large national insurers, four integrated delivery systems) from February 20-October 17, 2020. We identified patients hospitalized with COVID-19 according to five ICD-10-CM diagnosis code-based algorithms, which included combinations of codes U07.1, B97.29, general coronavirus codes, and diagnosis codes for severe symptoms. We calculated the positive predictive value (PPV) and sensitivity of each algorithm relative to laboratory test results. We stratified results by data source type and across three time periods: February 20-March 31 (Time A), April 1-30 (Time B), May 1-October 17 (Time C). RESULTS: The five algorithms identified between 34 806 and 47 293 patients across the study periods; 23% with known laboratory results contributed to PPV calculations. PPVs were high and similar across algorithms. PPV of U07.1 alone was stable around 93% for integrated delivery systems, but declined over time from 93% to 70% among national insurers. Overall PPV of U07.1 across all data partners was 94.1% (95% CI, 92.3%-95.5%) in Time A and 81.2% (95% CI, 80.1%-82.2%) in Time C. Sensitivity was consistent across algorithms and over time, at 94.9% (95% CI, 94.2%-95.5%). CONCLUSION: Our results support the use of code U07.1 to identify hospitalized COVID-19 patients in U.S. claims data.


Subject(s)
COVID-19 , Algorithms , COVID-19/diagnosis , COVID-19/epidemiology , COVID-19 Testing , Databases, Factual , Delivery of Health Care , Humans , International Classification of Diseases , SARS-CoV-2
9.
J Med Virol ; 94(4): 1550-1557, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1540145

ABSTRACT

International Statistical Classification of Disease and Related Health Problems, 10th Revision codes (ICD-10) are used to characterize cohort comorbidities. Recent literature does not demonstrate standardized extraction methods. OBJECTIVE: Compare COVID-19 cohort manual-chart-review and ICD-10-based comorbidity data; characterize the accuracy of different methods of extracting ICD-10-code-based comorbidity, including the temporal accuracy with respect to critical time points such as day of admission. DESIGN: Retrospective cross-sectional study. MEASUREMENTS: ICD-10-based-data performance characteristics relative to manual-chart-review. RESULTS: Discharge billing diagnoses had a sensitivity of 0.82 (95% confidence interval [CI]: 0.79-0.85; comorbidity range: 0.35-0.96). The past medical history table had a sensitivity of 0.72 (95% CI: 0.69-0.76; range: 0.44-0.87). The active problem list had a sensitivity of 0.67 (95% CI: 0.63-0.71; range: 0.47-0.71). On day of admission, the active problem list had a sensitivity of 0.58 (95% CI: 0.54-0.63; range: 0.30-0.68)and past medical history table had a sensitivity of 0.48 (95% CI: 0.43-0.53; range: 0.30-0.56). CONCLUSIONS AND RELEVANCE: ICD-10-based comorbidity data performance varies depending on comorbidity, data source, and time of retrieval; there are notable opportunities for improvement. Future researchers should clearly outline comorbidity data source and validate against manual-chart-review.


Subject(s)
COVID-19/diagnosis , Clinical Coding/standards , International Classification of Diseases/standards , COVID-19/epidemiology , COVID-19/virology , Clinical Coding/methods , Comorbidity , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Philadelphia , Reproducibility of Results , Retrospective Studies , SARS-CoV-2
10.
BMC Med Inform Decis Mak ; 21(Suppl 6): 206, 2021 11 09.
Article in English | MEDLINE | ID: covidwho-1508420

ABSTRACT

BACKGROUND: The International Classification of Diseases (ICD) has long been the main basis for comparability of statistics on causes of mortality and morbidity between places and over time. This paper provides an overview of the recently completed 11th revision of the ICD, focusing on the main innovations and their implications. MAIN TEXT: Changes in content reflect knowledge and perspectives on diseases and their causes that have emerged since ICD-10 was developed about 30 years ago. Changes in design and structure reflect the arrival of the networked digital era, for which ICD-11 has been prepared. ICD-11's information framework comprises a semantic knowledge base (the Foundation), a biomedical ontology linked to the Foundation and classifications derived from the Foundation. ICD-11 for Mortality and Morbidity Statistics (ICD-11-MMS) is the primary derived classification and the main successor to ICD-10. Innovations enabled by the new architecture include an online coding tool (replacing the index and providing additional functions), an application program interface to enable remote access to ICD-11 content and services, enhanced capability to capture and combine clinically relevant characteristics of cases and integrated support for multiple languages. CONCLUSIONS: ICD-11 was adopted by the World Health Assembly in May 2019. Transition to implementation is in progress. ICD-11 can be accessed at icd.who.int.


Subject(s)
Biological Ontologies , International Classification of Diseases , Global Health , Humans , Knowledge Bases
11.
BMC Bioinformatics ; 22(Suppl 6): 508, 2021 Oct 18.
Article in English | MEDLINE | ID: covidwho-1477258

ABSTRACT

BACKGROUND: The 10th and 9th revisions of the International Statistical Classification of Diseases and Related Health Problems (ICD10 and ICD9) have been adopted worldwide as a well-recognized norm to share codes for diseases, signs and symptoms, abnormal findings, etc. The international Consortium for Clinical Characterization of COVID-19 by EHR (4CE) website stores diagnosis COVID-19 disease data using ICD10 and ICD9 codes. However, the ICD systems are difficult to decode due to their many shortcomings, which can be addressed using ontology. METHODS: An ICD ontology (ICDO) was developed to logically and scientifically represent ICD terms and their relations among different ICD terms. ICDO is also aligned with the Basic Formal Ontology (BFO) and reuses terms from existing ontologies. As a use case, the ICD10 and ICD9 diagnosis data from the 4CE website were extracted, mapped to ICDO, and analyzed using ICDO. RESULTS: We have developed the ICDO to ontologize the ICD terms and relations. Different from existing disease ontologies, all ICD diseases in ICDO are defined as disease processes to describe their occurrence with other properties. The ICDO decomposes each disease term into different components, including anatomic entities, process profiles, etiological causes, output phenotype, etc. Over 900 ICD terms have been represented in ICDO. Many ICDO terms are presented in both English and Chinese. The ICD10/ICD9-based diagnosis data of over 27,000 COVID-19 patients from 5 countries were extracted from the 4CE. A total of 917 COVID-19-related disease codes, each of which were associated with 1 or more cases in the 4CE dataset, were mapped to ICDO and further analyzed using the ICDO logical annotations. Our study showed that COVID-19 targeted multiple systems and organs such as the lung, heart, and kidney. Different acute and chronic kidney phenotypes were identified. Some kidney diseases appeared to result from other diseases, such as diabetes. Some of the findings could only be easily found using ICDO instead of ICD9/10. CONCLUSIONS: ICDO was developed to ontologize ICD10/10 codes and applied to study COVID-19 patient diagnosis data. Our findings showed that ICDO provides a semantic platform for more accurate detection of disease profiles.


Subject(s)
COVID-19 , International Classification of Diseases , Data Analysis , Humans , SARS-CoV-2
12.
J Am Geriatr Soc ; 69(12): 3389-3396, 2021 12.
Article in English | MEDLINE | ID: covidwho-1476287

ABSTRACT

BACKGROUND: The COVID-19 pandemic delayed diagnosis and care for some acute conditions and reduced monitoring for some chronic conditions. It is unclear whether new diagnoses of chronic conditions such as dementia were also affected. We compared the pattern of incident Alzheimer's disease and related dementia (ADRD) diagnosis codes from 2017 to 2019 through 2020, the first pandemic year. METHODS: Retrospective cohort design, leveraging 2015-2020 data on all members 65 years and older with no prior ADRD diagnosis, enrolled in a large integrated healthcare system for at least 2 years. Incident ADRD was defined as the first ICD-10 code at any encounter, including outpatient (face-to-face, video, or phone), hospital (emergency department, observation, or inpatient), or continuing care (home, skilled nursing facility, and long-term care). We also examined incident ADRD codes and use of telehealth by age, sex, race/ethnicity, and spoken language. RESULTS: Compared to overall annual incidence rates for ADRD codes in 2017-2019, 2020 incidence was slightly lower (1.30% vs. 1.40%), partially compensating later in the year for reduced rates during the early months of the pandemic. No racial or ethnic group differences were identified. Telehealth ADRD codes increased fourfold, making up for a 39% drop from face-to-face outpatient encounters. Older age (85+) was associated with higher odds of receiving telecare versus face-to-face care in 2020 (OR:1.50, 95%CI: 1.25-1.80) and a slightly lower incidence of new codes; no racial/ethnic, sex, or language differences were identified in the mode of care. CONCLUSIONS: Rates of incident ADRD codes dropped early in the first pandemic year but rose again to near pre-pandemic rates for the year as a whole, as clinicians rapidly pivoted to telehealth. With refinement of protocols for remote dementia detection and diagnosis, health systems could improve access to equitable detection and diagnosis of ADRD going forward.


Subject(s)
Alzheimer Disease/epidemiology , COVID-19 , Delivery of Health Care, Integrated , Dementia/epidemiology , Aged , Alzheimer Disease/classification , COVID-19/epidemiology , California/epidemiology , Female , Humans , Incidence , International Classification of Diseases , Male , Pandemics , Quality of Health Care , Retrospective Studies , SARS-CoV-2 , Skilled Nursing Facilities , United States
13.
PLoS One ; 16(9): e0257183, 2021.
Article in English | MEDLINE | ID: covidwho-1410674

ABSTRACT

BACKGROUND: While potentially timesaving, there is no program to automatically transform diagnosis codes of the ICD-10 German modification (ICD-10-GM) into the injury severity score (ISS). OBJECTIVE: To develop a mapping method from ICD-10-GM into ICD-10 clinical modification (ICD-10-CM) to calculate the abbreviated injury scale (AIS) and ISS of each patient using the ICDPIC-R and to compare the manually and automatically calculated scores. METHODS: Between January 2019 and June 2021, the most severe AIS of each body region and the ISS were manually calculated using medical documentation and radiology reports of all major trauma patients of a German level I trauma centre. The ICD-10-GM codes of these patients were exported from the electronic medical data system SAP, and a Java program was written to transform these into ICD-10-CM codes. Afterwards, the ICDPIC-R was used to automatically generate the most severe AIS of each body region and the ISS. The automatically and manually determined ISS and AIS scores were then tested for equivalence. RESULTS: Statistical analysis revealed that the manually and automatically calculated ISS were significantly equivalent over the entire patient cohort. Further sub-group analysis, however, showed that equivalence could only be demonstrated for patients with an ISS between 16 and 24. Likewise, the highest AIS scores of each body region were not equal in the manually and automatically calculated group. CONCLUSION: Though achieving mapping results highly comparable to previous mapping methods of ICD-10-CM diagnosis codes, it is not unrestrictedly possible to automatically calculate the AIS and ISS using ICD-10-GM codes.


Subject(s)
Injury Severity Score , International Classification of Diseases , Adolescent , Adult , Aged , Aged, 80 and over , Automation , Child , Child, Preschool , Emergency Service, Hospital , Hip Fractures/diagnosis , Hip Fractures/pathology , Humans , Middle Aged , Observer Variation , Young Adult
15.
Vaccine ; 39(38): 5368-5375, 2021 09 07.
Article in English | MEDLINE | ID: covidwho-1377852

ABSTRACT

BACKGROUND: Anaphylaxis is a rare, serious allergic reaction. Its identification in large healthcare databases can help better characterize this risk. OBJECTIVE: To create an ICD-10 anaphylaxis algorithm, estimate its positive predictive values (PPVs) in a post-vaccination risk window, and estimate vaccination-attributable anaphylaxis rates in the Medicare Fee For Service (FFS) population. METHODS: An anaphylaxis algorithm with core and extended portions was constructed analyzing ICD-10 anaphylaxis claims data in Medicare FFS from 2015 to 2017. Cases of post-vaccination anaphylaxis among Medicare FFS beneficiaries were then identified from October 1, 2015 to February 28, 2019 utilizing vaccine relevant anaphylaxis ICD-10 codes. Information from medical records was used to determine true anaphylaxis cases based on the Brighton Collaboration's anaphylaxis case definition. PPVs were estimated for incident anaphylaxis and the subset of vaccine-attributable anaphylaxis within a 2-day post-vaccination risk window. Vaccine-attributable anaphylaxis rates in Medicare FFS were also estimated. RESULTS: The study recorded 66,572,128 vaccinations among 21,685,119 unique Medicare FFS beneficiaries. The algorithm identified a total of 190 suspected anaphylaxis cases within the 2-day post-vaccination window; of these 117 (62%) satisfied the core algorithm, and 73 (38%) additional cases satisfied the extended algorithm. The core algorithm's PPV was 66% (95% CI [56%, 76%]) for identifying incident anaphylaxis and 44% (95% CI [34%, 56%]) for vaccine-attributable anaphylaxis. The vaccine-attributable anaphylaxis incidence rate after any vaccination was 0.88 per million doses (95% CI [0.67, 1.16]). CONCLUSION: The ICD-10 claims algorithm for anaphylaxis allows the assessment of anaphylaxis risk in real-world data. The algorithm revealed vaccine-attributable anaphylaxis is rare among vaccinated Medicare FFS beneficiaries.


Subject(s)
Anaphylaxis , Vaccines , Aged , Algorithms , Anaphylaxis/chemically induced , Anaphylaxis/epidemiology , Humans , Incidence , International Classification of Diseases , Medicare , United States/epidemiology , Vaccines/adverse effects
17.
Drug Alcohol Depend ; 226: 108877, 2021 09 01.
Article in English | MEDLINE | ID: covidwho-1293716

ABSTRACT

INTRODUCTION: Little detailed sociodemographic information is available about how alcohol use and associated health care visits have changed during COVID-19. Therefore, we assessed how rates of emergency department (ED) visits due to alcohol have changed during COVID-19 by age and sex and for individuals living in urban and rural settings and low and high-income neighborhoods. METHODS: Our cohort included 13,660,516 unique Ontario residents between the ages of 10-105. We compared rates and characteristics of ED visits due to alcohol, identified using ICD-10 codes, from March 11-August 31 2020 to the same period in the prior 3 years. We used negative binomial regressions to examine to examine changes is visits during COVID-19 after accounting for temporal and seasonal trends. RESULTS: During COVID-19, the average monthly rate of ED visits due to alcohol decreased by 17.2 % (95 % CI -22.7, -11.3) from 50.5-40.9 visits per 100,000 individuals. In contrast, the proportion of all-cause ED visits due to alcohol increased by 11.4 % (95 % CI 7.7, 15.3) from 15.0 visits to 16.3 visits per 1000 all cause ED visits. Changes in ED visits due to alcohol were similar for men in women. Decreases in visits were larger for younger adults compared to older adults and pre-COVID-19 disparities in rates of ED visits due to alcohol between urban and rural settings and low and high-income neighborhoods widened. ED visits related to harms from acute intoxication showed the largest declines during COVID-19, particularly in younger adults and urban and high-income neighborhoods. CONCLUSION: ED visits due to alcohol decreased during the first six months of COVID-19, but to a lesser extent than decreases in all-cause ED visits. Our data suggest a widening of geographic and income-based disparities in alcohol harms in Ontario during COVID-19 which may require immediate and long-term interventions to mitigate.


Subject(s)
COVID-19 , Adolescent , Adult , Aged , Aged, 80 and over , Alcohol Drinking , Child , Emergency Service, Hospital , Female , Humans , International Classification of Diseases , Male , Middle Aged , SARS-CoV-2 , Young Adult
18.
J Hosp Med ; 16(6): 353-356, 2021 06.
Article in English | MEDLINE | ID: covidwho-1270269

ABSTRACT

The COVID-19 pandemic has dramatically disrupted the educational experience of medical trainees. However, a detailed characterization of exactly how trainees' clinical experiences have been affected is lacking. Here, we profile residents' inpatient clinical experiences across the four training hospitals of NYU's Internal Medicine Residency Program during the pandemic's first wave. We mined ICD-10 principal diagnosis codes attributed to residents from February 1, 2020, to May 31, 2020. We translated these codes into discrete medical content areas using a newly developed "crosswalk tool." Residents' clinical exposure was enriched in infectious diseases (ID) and cardiovascular disease content at baseline. During the pandemic's surge, ID became the dominant content area. Exposure to other content was dramatically reduced, with clinical diversity repopulating only toward the end of the study period. Such characterization can be leveraged to provide effective practice habits feedback, guide didactic and self-directed learning, and potentially predict competency-based outcomes for trainees in the COVID era.


Subject(s)
COVID-19 , Cardiology/education , Infectious Disease Medicine/education , Internship and Residency , Pandemics , Humans , International Classification of Diseases , New York City
20.
Eur J Pediatr ; 180(11): 3343-3357, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1242787

ABSTRACT

The study aims to identify 'missing' diagnoses amongst paediatric admissions during the UK's first national lockdown, compared with the previous 5 years. A retrospective observational cohort study of all children (0-15 years) attending for urgent care across Oxfordshire, during the first UK lockdown in 2020, compared to matched dates in 2015-2019, across two paediatric hospitals providing secondary care, including one with tertiary services. Our outcome measures were changes in numbers of patients attending and inpatient diagnoses (using ICD-10 classification) during the first 2020 lockdown, compared with the previous 5 years, were used. We found that total Emergency Department (ED) attendances (n = 4030) and hospital admissions (n = 1416) during the first UK lockdown were reduced by 56.8% and 59.4%, respectively, compared to 2015-2019 (5-year means n = 7446.8 and n = 2491.6, respectively). Proportions of patients admitted from ED and length of stay were similar across 2015-2020. ICD-10 diagnoses in lockdown of 2020 (n = 2843) versus matched 2015-2019 dates (n = 19,946) demonstrated significantly greater neoplasm diagnoses (p = 0.0123). Of diagnoses 'missing' in lockdown, 80% were categorised as infectious diseases or their sequelae and 20% were non-specific pains/aches/malaise and accidental injury/poisonings.Conclusions: Pandemic public health measures significantly altered paediatric presentations. Oxfordshire hospitals had a 58% reduction in ED attendances/inpatient admissions, with 'missing' diagnoses predominantly infection-related illnesses. These are likely driven by a combination of the following: (1) public health infection control measures successfully reducing disease transmission, (2) parents/carers keeping mild/self-limiting disease at home, and (3) pandemic-related healthcare anxieties. Prospective studies are needed to ensure referral pathways identify vulnerable children, those with social concerns, and avoid delayed presentation. What is Known: • Significant reductions of paediatric ED attendances and inpatient admissions are reported globally, throughout national and regional lockdowns for COVID-19. • Previous studies (supplemental table 5) examined only ED diagnoses or specific inpatient diagnoses during lockdown periods, demonstrating reductions of infectious diseases, accidents/injuries, and safeguarding referrals. What is New: • Using ICD-10 coding, robustly controlling for five historical years and adopting a hypothesis-independent analysis, demonstrating 80% of 'missing' inpatient diagnoses during national COVID-19 lockdown were infectious diseases or its sequelae, the remainder being non-specific aches/pains/malaise and accidental injuries/poisonings. • Greater numbers of neoplasms and other specific diagnoses were detected during lockdown, including greater documentation of co-morbidities and incidental findings.


Subject(s)
COVID-19 , Child , Emergency Service, Hospital , Humans , Infection Control , International Classification of Diseases , Retrospective Studies , SARS-CoV-2 , United Kingdom
SELECTION OF CITATIONS
SEARCH DETAIL