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COVID-19 cases in the United States, first reported on January 22, 2020, began to increase in March 2020 (1). The pandemic resulted in a substantial number of employed persons being [...]

In response to the then-evolving COVID-19 pandemic, a public health emergency was declared in the United States on January 31, 2020, and several closure and containment policies were subsequently put [...]

Purpose: Understanding emergency department (ED) use in adolescent and young adult (AYA) survivors could identify gaps in AYA survivorship. Methods: We conducted a cohort study of 7925 AYA survivors (aged 15–39 years at diagnosis) who were 2–5 years from diagnosis in 2006–2020 at Kaiser Permanente Southern California. We calculated ED utilization rates overall and by indication of the encounter (headache, cardiac issues, and suicide attempts). We estimated rate changes by survivorship year and patient factors associated with ED visit using a Poisson model. Results: Cohort was 65.4% women, 45.8% Hispanic, with mean age at diagnosis at 31.3 years. Overall, 38% of AYA survivors had ≥1 ED visit (95th percentile: 5 ED visits). Unadjusted ED rates declined from 374.2/1000 person-years (PY) in Y2 to 327.2 in Y5 (p change < 0.001). Unadjusted rates declined for headache, cardiac issues, and suicide attempts. Factors associated with increased ED use included: age 20–24 at diagnosis [relative risk (RR) = 1.30, 95% CI 1.09–1.56 vs. 35–39 years]; female (RR = 1.27, 95% CI 1.11–1.47 vs. male); non-Hispanic Black race/ethnicity (RR 1.64, 95% CI 1.38–1.95 vs. non-Hispanic white); comorbidity (RR = 1.34, 95% CI 1.16–1.55 for 1 and RR 1.80, 95% CI 1.40–2.30 for 2+ vs. none); and public insurance (RR = 1.99, 95% CI 1.70–2.32 vs. private). Compared with thyroid cancer, cancers associated with increased ED use were breast (RR = 1.45, 95% CI 1.24–1.70), cervical (RR = 2.18, 95% CI 1.76–2.71), colorectal (RR = 2.34, 95% CI 1.94–2.81), and sarcoma (RR = 1.39, 95% CI 1.03–1.88). Conclusion: ED utilization declined as time from diagnosis elapsed, but higher utilization was associated with social determinants of health and cancer types. [ABSTRACT FROM AUTHOR]

Simple Summary: Adolescents and young adults (AYAs) experiencing cancer have support needs that differ from older adult cancer patients. The aim of our qualitative study was to determine the holistic needs of AYA cancer survivors and to develop a patient-centered conceptual model of AYA survivorship care. Analysis of our interview results reveals eight key domains critical to holistic patient-centered AYA survivorship care. We offer a conceptual model that differs from current conceptual models of AYA survivorship care by centering the patient and their support systems, emphasizing the need for continuing supportive navigation, and the importance of repeated support along the identified domains over time. Purpose: Conceptual models provide frameworks to illustrate relationships among patient-, provider-, system-, and community-level factors that inform care delivery and research. Existing models of cancer survivorship care focus largely on pediatric or adult populations whose needs differ from adolescents and young adults (AYAs). We developed a patient-centered conceptual model of AYA survivorship care. Methods: We conducted a narrative literature review of current conceptual and theoretical models of care. We engaged AYA cancer survivors (n = 25) in semi-structured one-hour telephone interviews. Most participants were in their 20s and 30s, and the majority (84%) were women. Recruitment was stratified by age and time since cancer diagnosis. We conducted a thematic analysis of interview transcripts to identify themes that exemplified patient-centered care. Results: Most participants identified as white and female. Leukemia and breast cancer were the most common cancer types. Main themes included the need for (1) care coordination, (2) ongoing mental health support, (3) connection to AYA peer support, (4) support during fertility preservation efforts, (5) support with financial burden, (6) support for quality of life, (7) information about and support with side effects and late effects, and (8) attention to the unique needs of young adults. Conclusions: We present a patient-centered conceptual model of AYA survivorship care needs that can inform future cancer care delivery and research. [ABSTRACT FROM AUTHOR]

Background We assessed associations between binding antibody (bAb) concentration <5 days from symptom onset and testing positive for COVID-19 among patients in a test-negative study. Methods From October 2021 to June 2022, study sites in 7 states enrolled patients aged ≥6 months presenting with acute respiratory illness. Respiratory specimens were tested for SARS-CoV-2. In blood specimens, we measured concentrations of anti-SARS-CoV-2 antibodies against the spike protein receptor binding domain (RBD) and nucleocapsid antigens from the ancestral strain in standardized bAb units (BAU). Percentage change in odds of COVID-19 by increasing anti-RBD bAb was estimated via logistic regression as (1 – adjusted odds ratio of COVID-19) × 100, adjusting for COVID-19 mRNA vaccine doses, age, site, and high-risk exposure. Results Out of 2018 symptomatic patients, 662 (33%) tested positive for acute SARS-CoV-2 infection. Geometric mean RBD bAb levels were lower among COVID-19 cases than SARS-CoV-2 test-negative controls during the Delta-predominant period (112 vs 498 BAU/mL) and Omicron-predominant period (823 vs 1189 BAU/mL). Acute-phase ancestral spike RBD bAb levels associated with 50% lower odds of COVID-19 were 1968 BAU/mL against Delta and 3375 BAU/mL against Omicron; thresholds may differ in other laboratories. Conclusions During acute illness, antibody concentrations against ancestral spike RBD were associated with protection against COVID-19. [ABSTRACT FROM AUTHOR]

We describe humoral immune responses in 105 ambulatory patients with laboratory‐confirmed SARS‐CoV‐2 Omicron variant infection. In dried blood spot (DBS) collected within 5 days of illness onset and during convalescence, we measured binding antibody (bAb) against ancestral spike protein receptor binding domain (RBD) and nucleocapsid (N) protein using a commercial multiplex bead assay. Geometric mean bAb concentrations against RBD increased by a factor of 2.5 from 1258 to 3189 units/mL and by a factor of 47 against N protein from 5.5 to 259 units/mL between acute illness and convalescence; lower concentrations were associated with greater geometric mean ratios. Paired DBS specimens may be used to evaluate humoral response to SARS‐CoV‐2 infection. [ABSTRACT FROM AUTHOR]

Background Annual surveillance mammography is recommended for women with a personal history of breast cancer. Risk prediction models that estimate mammography failures such as interval second breast cancers could help to tailor surveillance imaging regimens to women's individual risk profiles. Methods In a cohort of women with a history of breast cancer receiving surveillance mammography in the Breast Cancer Surveillance Consortium in 1996-2019, we used Least Absolute Shrinkage and Selection Operator (LASSO)-penalized regression to estimate the probability of an interval second cancer (invasive cancer or ductal carcinoma in situ) in the 1 year after a negative surveillance mammogram. Based on predicted risks from this one-year risk model, we generated cumulative risks of an interval second cancer for the five-year period after each mammogram. Model performance was evaluated using cross-validation in the overall cohort and within race and ethnicity strata. Results In 173 290 surveillance mammograms, we observed 496 interval cancers. One-year risk models were well-calibrated (expected/observed ratio = 1.00) with good accuracy (area under the receiver operating characteristic curve = 0.64). Model performance was similar across race and ethnicity groups. The median five-year cumulative risk was 1.20% (interquartile range 0.93%-1.63%). Median five-year risks were highest in women who were under age 40 or pre- or perimenopausal at diagnosis and those with estrogen receptor-negative primary breast cancers. Conclusions Our risk model identified women at high risk of interval second breast cancers who may benefit from additional surveillance imaging modalities. Risk models should be evaluated to determine if risk-guided supplemental surveillance imaging improves early detection and decreases surveillance failures. [ABSTRACT FROM AUTHOR]

Background: The 2022–23 US influenza season peaked early in fall 2022. Methods: Late‐season influenza vaccine effectiveness (VE) against outpatient, laboratory‐confirmed influenza was calculated among participants of the US Influenza VE Network using a test‐negative design. Results: Of 2561 participants enrolled from December 12, 2022 to April 30, 2023, 91 laboratory‐confirmed influenza cases primarily had A(H1N1)pdm09 (6B.1A.5a.2a.1) or A(H3N2) (3C.2a1b.2a.2b). Overall, VE was 30% (95% confidence interval −9%, 54%); low late‐season activity precluded estimation for most subgroups. Conclusions: 2022–23 late‐season outpatient influenza VE was not statistically significant. Genomic characterization may improve the identification of influenza viruses that circulate postinfluenza peak. [ABSTRACT FROM AUTHOR]

In the United States, annual influenza vaccination is recommended for all persons aged ≥6 months. Using data from four vaccine effectiveness (VE) networks during the 2023-24 influenza season, interim influenza VE was estimated among patients aged ≥6 months with acute respiratory illness-associated medical encounters using a test-negative casecontrol study design. Among children and adolescents aged 6 months-17 years, VE against influenza-associated outpatient visits ranged from 59% to 67% and against influenza-associated hospitalization ranged from 52% to 61%. Among adults aged ≥18 years, VE against influenza-associated outpatient visits ranged from 33% to 49% and against hospitalization from 41% to 44%. VE against influenza A ranged from 46% to 59% for children and adolescents and from 27% to 46% for adults across settings. VE against influenza B ranged from 64% to 89% for pediatric patients in outpatient settings and from 60% to 78% for all adults across settings. These findings demonstrate that the 2023-24 seasonal influenza vaccine is effective at reducing the risk for medically attended influenza virus infection. CDC recommends that all persons aged ≥6 months who have not yet been vaccinated this season get vaccinated while influenza circulates locally. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Documenting trends in cancer incidence and survival is a national priority. This study estimated age- and sex-adjusted incidence and 5-year relative survival among patients with cancer diagnosed within Kaiser Permanente compared to Surveillance, Epidemiology, and End Results (SEER) estimates. METHODS: The cohort included Kaiser Permanente health plan members diagnosed with breast (BC), colorectal (CRC), or lung cancer (LC) between January 1, 1999 and December 31, 2018. Incidence was computed as age-adjusted rates per 100,000 member-years. SEER*Stat was used to compute 5-year relative survival. RESULTS: Kaiser Permanente BC incidence rates were persistently higher than SEER from 2004 (126.5 [95% confidence interval (CI) = 123.2-129.9] vs 122.6 [95% CI = 121.3-123.2]) through 2013 (132.06 [95% CI = 129.5-135.7] vs 126.7 [95% CI = 125.9-127.5]). Kaiser Permanente CRC and LC incidence rates were lower than SEER for all years except 2008, showing a spike in CRC incidence (51.5 [95% CI = 49.9-53.0] vs 46.1 [95% CI = 45.7-46.4]). Kaiser Permanente BC, CRC, and LC survival estimates for all stages were higher than SEER. CONCLUSIONS: Incidence rates for all-stage and localized-stage BC were consistently higher for Kaiser Permanente than for SEER. CRC and LC rates were lower. Kaiser Permanente survival rates were consistently higher than for SEER. The strengths of these findings are associated with the ability to capture "gold-standard" cancer registry data on defined Kaiser Permanente populations. However, findings should be interpreted cautiously given differences in the underlying populations and secular and regional differences between Kaiser Permanente and SEER. The Kaiser Permanente population is younger and more racially diverse than SEER aggregate populations, and Kaiser Permanente members are insured with access to preventive care (eg, smoking cessation programs, cancer screening). [ABSTRACT FROM AUTHOR]

Background Imaging tests are one of the most frequently used diagnostic modalities in healthcare, but the benefits of their direct impacts on clinical decisionmaking have been countered by concerns that they can be overused. Assessing the relative value of imaging tests has largely focused on measures of test accuracy, which overlooks more comprehensive benefits and risks of imaging tests, particularly their impact on patient-centred outcomes (PCOs). We present the findings of the Patient Reported Outcomes of Diagnostics (PROD) research study in response to a methodological gap in the area of diagnostic test comparative effectiveness research. Methods Over a 3-year period, the PROD Study engaged with multiple stakeholders to identify existing conceptual models related to PCOs for imaging testing, conducted primary research and evidence synthesis, and developed consensus recommendations to describe and categorise PCOs related to imaging testing. Results The PROD framework categorises PCOs from imaging studies within four main domains: information or knowledge yielded, physical impact, emotional outcomes and test burden. PCOs interact with each other and influence effects across domains, and can be modified by factors related to the patient, clinical situation, healthcare team and the testing environment. Conclusions Using PCOs to inform healthcare decisionmaking will require ways of collating and presenting information on PCOs in ways that can inform patient- provider decision-making, and developing methods to determine the relative importance of outcomes (including test accuracy) to one another. [ABSTRACT FROM AUTHOR]

Background: We estimated combined protection conferred by prior SARS‐CoV‐2 infection and COVID‐19 vaccination against COVID‐19‐associated acute respiratory illness (ARI). Methods: During SARS‐CoV‐2 Delta (B.1.617.2) and Omicron (B.1.1.529) variant circulation between October 2021 and April 2022, prospectively enrolled adult patients with outpatient ARI had respiratory and filter paper blood specimens collected for SARS‐CoV‐2 molecular testing and serology. Dried blood spots were tested for immunoglobulin‐G antibodies against SARS‐CoV‐2 nucleocapsid (NP) and spike protein receptor binding domain antigen using a validated multiplex bead assay. Evidence of prior SARS‐CoV‐2 infection also included documented or self‐reported laboratory‐confirmed COVID‐19. We used documented COVID‐19 vaccination status to estimate vaccine effectiveness (VE) by multivariable logistic regression by prior infection status. Results: Four hundred fifty‐five (29%) of 1577 participants tested positive for SARS‐CoV‐2 infection at enrollment; 209 (46%) case‐patients and 637 (57%) test‐negative patients were NP seropositive, had documented previous laboratory‐confirmed COVID‐19, or self‐reported prior infection. Among previously uninfected patients, three‐dose VE was 97% (95% confidence interval [CI], 60%–99%) against Delta, but not statistically significant against Omicron. Among previously infected patients, three‐dose VE was 57% (CI, 20%–76%) against Omicron; VE against Delta could not be estimated. Conclusions: Three mRNA COVID‐19 vaccine doses provided additional protection against SARS‐CoV‐2 Omicron variant‐associated illness among previously infected participants. [ABSTRACT FROM AUTHOR]

Background: In women with previously treated breast cancer, occurrence and timing of second breast cancers have implications for surveillance. The authors examined the timing of second breast cancers by primary cancer estrogen receptor (ER) status in the Breast Cancer Surveillance Consortium. Methods: Women who were diagnosed with American Joint Commission on Cancer stage I–III breast cancer were identified within six Breast Cancer Surveillance Consortium registries from 2000 to 2017. Characteristics collected at primary breast cancer diagnosis included demographics, ER status, and treatment. Second breast cancer events included subsequent ipsilateral or contralateral breast cancers diagnosed >6 months after primary diagnosis. The authors examined cumulative incidence and second breast cancer rates by primary cancer ER status during 1–5 versus 6–10 years after diagnosis. Results: At 10 years, the cumulative second breast cancer incidence was 11.8% (95% confidence interval [CI], 10.7%–13.1%) for women with ER‐negative disease and 7.5% (95% CI, 7.0%–8.0%) for those with ER‐positive disease. Women with ER‐negative cancer had higher second breast cancer rates than those with ER‐positive cancer during the first 5 years of follow‐up (16.0 per 1000 person‐years [PY]; 95% CI, 14.2–17.9 per 1000 PY; vs. 7.8 per 1000 PY; 95% CI, 7.3–8.4 per 1000 PY, respectively). After 5 years, second breast cancer rates were similar for women with ER‐negative versus ER‐positive breast cancer (12.1 per 1000 PY; 95% CI, 9.9–14.7; vs. 9.3 per 1000 PY; 95% CI, 8.4–10.3 per 1000 PY, respectively). Conclusions: ER‐negative primary breast cancers are associated with a higher risk of second breast cancers than ER‐positive cancers during the first 5 years after diagnosis. Further study is needed to examine the potential benefit of more intensive surveillance targeting these women in the early postdiagnosis period. Women with estrogen receptor‐negative primary breast cancers are at higher risk of second breast cancers than women with estrogen receptor‐positive cancers during the first 5 years after diagnosis. Further study is needed to examine the potential benefit of more intensive surveillance targeting these women in the early postdiagnosis period. [ABSTRACT FROM AUTHOR]

Background In the United States, influenza activity during the 2021–2022 season was modest and sufficient enough to estimate influenza vaccine effectiveness (VE) for the first time since the beginning of the coronavirus disease 2019 pandemic. We estimated influenza VE against laboratory-confirmed outpatient acute illness caused by predominant A(H3N2) viruses. Methods Between October 2021 and April 2022, research staff across 7 sites enrolled patients aged ≥6 months seeking outpatient care for acute respiratory illness with cough. Using a test-negative design, we assessed VE against influenza A(H3N2). Due to strong correlation between influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination, participants who tested positive for SARS-CoV-2 were excluded from VE estimations. Estimates were adjusted for site, age, month of illness, race/ethnicity, and general health status. Results Among 6260 participants, 468 (7%) tested positive for influenza only, including 440 (94%) for A(H3N2). All 206 sequenced A(H3N2) viruses were characterized as belonging to genetic group 3C.2a1b subclade 2a.2, which has antigenic differences from the 2021–2022 season A(H3N2) vaccine component that belongs to clade 3C.2a1b subclade 2a.1. After excluding 1948 SARS-CoV-2–positive patients, 4312 patients were included in analyses of influenza VE; 2463 (57%) were vaccinated against influenza. Effectiveness against A(H3N2) for all ages was 36% (95% confidence interval, 20%–49%) overall. Conclusions Influenza vaccination in 2021–2022 provided protection against influenza A(H3N2)-related outpatient visits among young persons. [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) approaches facilitate risk prediction model development using high-dimensional predictors and higher-order interactions at the cost of model interpretability and transparency. We compared the relative predictive performance of statistical and ML models to guide modeling strategy selection for surveillance mammography outcomes in women with a personal history of breast cancer (PHBC). Methods: We cross-validated seven risk prediction models for two surveillance outcomes, failure (breast cancer within 12 months of a negative surveillance mammogram) and benefit (surveillance-detected breast cancer). We included 9,447 mammograms (495 failures, 1,414 benefits, and 7,538 nonevents) from years 1996 to 2017 using a 1:4 matched case-control samples of women with PHBC in the Breast Cancer Surveillance Consortium. We assessed model performance of conventional regression, regularized regressions (LASSO and elastic-net), and ML methods (random forests and gradient boosting machines) by evaluating their calibration and, among well-calibrated models, comparing the area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI). Results: LASSO and elastic-net consistently provided well-calibrated predicted risks for surveillance failure and benefit. The AUCs of LASSO and elastic-net were both 0.63 (95% CI, 0.60-0.66) for surveillance failure and 0.66 (95% CI, 0.64-0.68) for surveillance benefit, the highest among well-calibrated models. Conclusions: For predicting breast cancer surveillance mammography outcomes, regularized regression outperformed other modeling approaches and balanced the trade-off between model flexibility and interpretability. Impact: Regularized regression may be preferred for developing risk prediction models in other contexts with rare outcomes, similar training sample sizes, and low-dimensional features. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to develop a prioritization strategy for scheduling immediate screening mammographic interpretation and possible diagnostic evaluation.Methods: A population-based cohort with screening mammograms performed from 2012 to 2020 at 126 radiology facilities from 7 Breast Cancer Surveillance Consortium registries was identified. Classification trees identified combinations of clinical history (age, BI-RADS® density, time since prior mammogram, history of false-positive recall or biopsy result), screening modality (digital mammography, digital breast tomosynthesis), and facility characteristics (profit status, location, screening volume, practice type, academic affiliation) that grouped screening mammograms by recall rate, with ≥12/100 considered high and ≥16/100 very high. An efficiency ratio was estimated as the percentage of recalls divided by the percentage of mammograms.Results: The study cohort included 2,674,051 screening mammograms in 925,777 women, with 235,569 recalls. The most important predictor of recall was time since prior mammogram, followed by age, history of false-positive recall, breast density, history of benign biopsy, and screening modality. Recall rates were very high for baseline mammograms (21.3/100; 95% confidence interval, 19.7-23.0) and high for women with ≥5 years since prior mammogram (15.1/100; 95% confidence interval, 14.3-16.1). The 9.2% of mammograms in subgroups with very high and high recall rates accounted for 19.2% of recalls, an efficiency ratio of 2.1 compared with a random approach. Adding women <50 years of age with dense breasts accounted for 20.3% of mammograms and 33.9% of recalls (efficiency ratio = 1.7). Results including facility-level characteristics were similar.Conclusions: Prioritizing women with baseline mammograms or ≥5 years since prior mammogram for immediate interpretation and possible diagnostic evaluation could considerably reduce the number of women needing to return for diagnostic imaging at another visit. [ABSTRACT FROM AUTHOR]