Effect of mild COVID-19 on health-related quality of life.

Background: Little is known about the effects of a mild SARS-CoV-2 infection on health-related quality of life. Methods: This prospective observational study of symptomatic adults (18-87 years) who sought outpatient care for an acute respiratory illness, was conducted from 3/30/2020 to 4/30/2021. Participants completed the Short Form Health Survey (SF-12) at enrollment and 6-8 weeks later, to report their physical and mental health function levels as measured by the physical health and mental health composite scores (PHC and MHC, respectively). PHC and MHC scores for COVID-19 cases and non-COVID cases were compared using t-tests. Multivariable regression modeling was used to determine predictors of physical and mental health function at follow-up. Results: Of 2301 enrollees, 426 COVID-19 cases and 547 non-COVID cases completed both surveys. PHC improved significantly from enrollment to follow-up for both COVID-19 cases (5.4 ± 0.41; P < 0.001) and non-COVID cases (3.3 ± 0.32; P < 0.001); whereas MHC improved significantly for COVID-19 cases (1.4 ± 0.51; P < 0.001) and decreased significantly for non-COVID cases (-0.8 ± 0.37; P < 0.05). Adjusting for enrollment PHC, the most important predictors of PHC at follow-up included male sex (ß = 1.17; SE = 0.5; P = 0.021), having COVID-19 (ß = 1.99; SE = 0.54; P < 0.001); and non-white race (ß = -2.01; SE = 0.70; P = 0.004). Adjusting for enrollment MHC, the most important predictors of MHC at follow-up included male sex (ß = 1.92; SE = 0.63; P = 0.002) and having COVID-19 (ß = 2.42; SE = 0.67; P < 0.001). Conclusion: Both COVID-19 cases and non-COVID cases reported improved physical health function at 6-8 weeks' convalescence; whereas mental health function improved among COVID-19 cases but declined among non-COVID cases. Both physical and mental health functioning were significantly better among males with COVID-19 than females.

Agreement among sources of adult influenza vaccination in the age of immunization information systems.

INTRODUCTION: Many vaccination studies rely on self-reported vaccination status, with its inherent biases. Accuracy of influenza vaccination self-report has been evaluated periodically, typically using the medical record as the gold standard. The burgeoning of electronic medical records (EMRs) and immunization information systems (IISs) and the rise of adult vaccine administration in community pharmacies suggest the need for a reevaluation of self-reported vaccination status. METHODS: Vaccination data from self-report, the state IIS, the health system EMR and other sources were compared for participants in outpatient and inpatient influenza vaccine effectiveness studies for four seasons (2016-2017 to 2019-2020). Agreement among the sources was calculated along with sensitivity and specificity. Tests for trend assessed changes in completeness of the Pennsylvania - Statewide IIS (PA-SIIS) data over time. RESULTS: With self-report as the gold standard, agreement with the local EMR, PA-SIIS, and all sources was 62%, 77% and 85%, respectively. Sensitivity of the EMR was 42% (95% CI = 41, 43) and specificity was 91% (90, 92). With PA-SIIS-as the gold standard, agreement with the local EMR and all sources was 77% and 78%, respectively. Sensitivity of all sources combined was 96% (95, 97) and specificity was (63% (62, 64). Capture of influenza vaccinations in the IIS has not consistently improved over time, with a significant increase among children (P = 0.001), no change among working-age adults and a decrease among older adults (P = 0.004). However, PA-SIIS provided the largest percentage of verified vaccines (69.3%) compared with EMR (43.3%) and other sources (12.4%). CONCLUSION: Both self-report and PA-SIIS are good estimates of actual vaccine uptake. When high accuracy data are required, such as for vaccine effectiveness studies, triangulation using multiple sources should be conducted.

Proposed clinical indicators for efficient screening and testing for COVID-19 infection using Classification and Regression Trees (CART) analysis.

The introduction and rapid transmission of SARS-CoV-2 in the United States resulted in methods to assess, mitigate, and contain the resulting COVID-19 disease derived from limited knowledge. Screening for testing has been based on symptoms typically observed in inpatients, yet outpatient symptoms may differ. Classification and regression trees recursive partitioning created a decision tree classifying participants into laboratory-confirmed cases and non-cases. Demographic and symptom data from patients ages 18-87 years enrolled from March 29-June 8, 2020 were included. Presence or absence of SARS-CoV-2 was the target variable. Of 832 tested, 77 (9.3%) tested positive. Cases significantly more often reported diarrhea (12 percentage points (PP)), fever (15 PP), nausea/vomiting (9 PP), loss of taste/smell (52 PP), and contact with a COVID-19 case (54 PP), but less frequently reported sore throat (-27 PP). The 4-terminal node optimal tree had sensitivity of 69%, specificity of 78%, positive predictive value of 20%, negative predictive value of 97%, and AUC of 76%. Among those referred for testing, negative responses to two questions could classify about half (49%) of tested persons with low risk for SARS-CoV-2 and would save limited testing resources. Outpatient symptoms of COVID-19 appear to be broader than the inpatient syndrome.Initial supplies of anticipated COVID-19 vaccines may be limited and administration of first such available vaccines may need to be prioritized for essential workers, the most vulnerable, or those likely to have a robust response to vaccine. Another priority group could be those not previously infected. Those who screen out of testing may be less likely to have been infected by SARS-CoV-2 virus thus may be prioritized for vaccination when supplies are limited.

Proposed Clinical Indicators for Efficient Screening and Testing for COVID-19 Infection from Classification and Regression Trees (CART) Analysis.

BACKGROUND: The introduction and rapid transmission of SARS CoV2 in the United States resulted in implementation of methods to assess, mitigate and contain the resulting COVID-19 disease based on limited knowledge. Screening for testing has been based on symptoms typically observed in inpatients, yet outpatient symptom complexes may differ. METHODS: Classification and regression trees (CART) recursive partitioning created a decision tree classifying enrollees into laboratory-confirmed cases and non-cases. Demographic and symptom data from patients ages 18-87 years who were enrolled from March 29-April 26, 2020 were included. Presence or absence of SARSCoV2 was the target variable. RESULTS: Of 736 tested, 55 were positive for SARS-CoV2. Cases significantly more often reported chills, loss of taste/smell, diarrhea, fever, nausea/vomiting and contact with a COVID-19 case, but less frequently reported shortness of breath and sore throat. A 7-terminal node tree with a sensitivity of 96% and specificity of 53%, and an AUC of 78% was developed. The positive predictive value for this tree was 14% while the negative predictive value was 99%. Almost half (44%) of the participants could be ruled out as likely non-cases without testing. DISCUSSION: Among those referred for testing, negative responses to three questions could classify about half of tested persons with low risk for SARS-CoV2 and would save limited testing resources. These questions are: was the patient in contact with a COVID-19 case? Has the patient experienced 1) a loss of taste or smell; or 2) nausea or vomiting? The outpatient symptoms of COVID-19 appear to be broader than the well-known inpatient syndrome.

Influenza vaccine effectiveness among outpatients in the US Influenza Vaccine Effectiveness Network by study site 2011-2016.

BACKGROUND: Influenza vaccination is recommended for all US residents aged ≥6 months. Vaccine effectiveness (VE) varies by age, circulating influenza strains, and the presence of high-risk medical conditions. We examined site-specific VE in the US Influenza VE Network, which evaluates annual influenza VE at ambulatory clinics in geographically diverse sites. METHODS: Analyses were conducted on 27 180 outpatients ≥6 months old presenting with an acute respiratory infection (ARI) with cough of ≤7-day duration during the 2011-2016 influenza seasons. A test-negative design was used with vaccination status defined as receipt of ≥1 dose of any influenza vaccine according to medical records, registries, and/or self-report. Influenza infection was determined by reverse-transcription polymerase chain reaction. VE estimates were calculated using odds ratios from multivariable logistic regression models adjusted for age, sex, race/ethnicity, time from illness onset to enrollment, high-risk conditions, calendar time, and vaccination status-site interaction. RESULTS: For all sites combined, VE was statistically significant every season against all influenza and against the predominant circulating strains (VE = 19%-50%) Few differences among four sites in the US Flu VE Network were evident in five seasons. However, in 2015-16, overall VE in one site was 24% (95% CI = -4%-44%), while VE in two other sites was significantly higher (61%, 95% CI = 49%-71%; P = .002, and 53%, 95% CI = 33,67; P = .034). CONCLUSION: With few exceptions, site-specific VE estimates aligned with each other and overall VE estimates. Observed VE may reflect inherent differences in community characteristics of the sites and highlights the importance of diverse settings for studying influenza vaccine effectiveness.

Physical health and mental health functional status during and following hospitalization for an acute respiratory illness.

Background: Influenza is a serious respiratory illness causing thousands of hospitalizations annually. This study used the Short Form 12 (SF-12) to evaluate physical and mental health status during and post hospitalization for an acute respiratory illness (ARI). Methods: Adults ≥18 years of age enrolled in the Hospitalized Adult Influenza Vaccine Effectiveness Network study - Pittsburgh site in the 2017-2018 and 2018-2019 influenza seasons with an ARI with cough of ≤10 days' duration were eligible. Enrollees were included regardless of respiratory pathogen identified by respiratory viral panel testing of nasopharyngeal specimens. Respondents completed the SF-12 at enrollment and 3-14 weeks later. Respondents were grouped using discriminant cluster analysis based on SF-12 individual scores and age. Linear regression was used to predict convalescent physical and mental health composite scores. Results: Of 72 enrollees who completed both surveys, 35 were grouped as the high functioning group (HFG), 12 as the low functioning group (LFG) and 25 as the medium functioning group (MFG). At enrollment, the LFG more frequently reported body aches and confusion, lower pre-illness physical activity levels and other measures of physical function than the HFG (P < 0.016). At approximately 5 weeks post enrollment, the HFG reported significant decrements in most SF-12 individual scores and overall physical health (-4.26 ±â€¯8.1; P = 0.017) and mental health (-5.98 ±â€¯10.5; P = 0.011) composite scores. Changes in mental but not physical composite scores from enrollment to convalescence differed significantly (P = 0.016) between HFG and LFG. Conclusions: Although their enrollment and convalescent SF-12 scores were higher, HFG reported larger losses in mental function during an ARI hospitalization than groups with lower enrollment SF-12 scores.

Determination of Eligibility for Influenza Research: A Clinical Informatics Approach.

BACKGROUND: A clinical informatics algorithm (CIA) was developed to systematically identify potential enrollees for a test-negative, case-control study to determine influenza vaccine effectiveness, to improve enrollment over manual records review. Further testing may enhance the CIA for increased efficiency. METHODS: The CIA generated a daily screening list by querying all medical record databases for patients admitted in the last 3 days, using specified terms and diagnosis codes located in admission notes, emergency department notes, chief complaint upon registration, or presence of a respiratory viral panel charge or laboratory result (RVP). Classification and regression tree analysis (CART) and multivariable logistic regression were used to refine the algorithm. RESULTS: Using manual records review, 204 patients (<4/day) were approached and 144 were eligible in the 2014-2015 season compared with 3531 (12/day) patients who were approached and 1136 who were eligible in the 2016-2017 season using a CIA. CART analysis identified RVP as the most important indicator from the CIA list for determining eligibility, identifying 65%-69% of the samples and predicting 1587 eligible patients. RVP was confirmed as the most significant predictor in regression analysis, with an odds ratio (OR) of 4.9 (95% confidence interval [CI], 4.0-6.0). Other significant factors were indicators in admission notes (OR, 2.3 [95% CI, 1.9-2.8]) and emergency department notes (OR, 1.8 [95% CI, 1.4-2.3]). CONCLUSIONS: This study supports the benefits of a CIA to facilitate recruitment of eligible participants in clinical research over manual records review. Logistic regression and CART identified potential eligibility screening criteria reductions to improve the CIA's efficiency.

Viral infections in outpatients with medically attended acute respiratory illness during the 2012-2013 influenza season.

BACKGROUND: While it is known that acute respiratory illness (ARI) is caused by an array of viruses, less is known about co-detections and the resultant comparative symptoms and illness burden. This study examined the co-detections, the distribution of viruses, symptoms, and illness burden associated with ARI between December 2012 and March 2013. METHODS: Outpatients with ARI were assayed for presence of 18 viruses using multiplex reverse transcriptase polymerase chain reaction (MRT-PCR) to simultaneously detect multiple viruses. RESULTS: Among 935 patients, 60% tested positive for a single virus, 9% tested positive for ≥1 virus and 287 (31%) tested negative. Among children (<18 years), the respective distributions were 63%, 14%, and 23%; whereas for younger adults (18-49 years), the distributions were 58%, 8%, and 34% and for older adults (≥50 years) the distributions were 61%, 5%, and 32% (P < 0.001). Co-detections were more common in children than older adults (P = 0.01), and less frequent in households without children (P = 0.003). Most frequently co-detected viruses were coronavirus, respiratory syncytial virus, and influenza A virus. Compared with single viral infections, those with co-detections less frequently reported sore throat (P = 0.01), missed fewer days of school (1.1 vs. 2 days; P = 0.04), or work (2 vs. 3 days; P = 0.03); other measures of illness severity did not vary. CONCLUSIONS: Among outpatients with ARI, 69% of visits were associated with a viral etiology. Co-detections of specific clusters of viruses were observed in 9% of ARI cases particularly in children, were less frequent in households without children, and were less symptomatic (e.g., lower fever) than single infections.