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Abstract To understand the roles of acute-phase viral dynamics and host immune responses in post-acute sequelae of SARS-CoV-2 infection (PASC), we enrolled 136 participants within 5 days of their first positive SARS-CoV-2 real-time PCR test. Participants self-collected up to 21 nasal specimens within the first 28 days post-symptom onset; interviewer-administered questionnaires and blood samples were collected at enrollment, days 9, 14, 21, 28, and month 4 and 8 post-symptom onset. Defining PASC as the presence of any COVID-associated symptom at their 4-month visit, we compared viral markers (quantity and duration of nasal viral RNA load, infectious viral load, and plasma N-antigen level) and host immune markers (IL-6, IL-10, TNF-α, IFN-α, IFN-γ, MCP, IP-10, and Spike IgG) over the acute period. Compared to those who fully recovered, those reporting PASC demonstrated significantly higher maximum levels of SARS-CoV-2 RNA and N-antigen, burden of RNA and infectious viral shedding, and lower Spike-specific IgG levels within 9 days post-illness onset. No significant differences were identified among a panel of host immune markers. Our results suggest early viral dynamics and the associated host immune responses play a role in the pathogenesis of PASC, highlighting the importance of understanding early biological markers in the natural history of PASC.

Abstract Background There are limited population-representative data that describe the potential burden of Post-COVID conditions (PCC) in Mexico. We estimated the prevalence of PCC overall and by sociodemographic characteristics among a representative sample of adults previously diagnosed with COVID-19 in Mexico. We additionally, characterized the PCC symptoms, and estimated the association between diagnosed type-2 diabetes and hypertension with PCC. Methods We used data from the 2021 National Health and Nutrition Survey in Mexico, a nationally and regionally representative survey, from August 1st to October 31st, 2021. Using the WHO definition, we estimated the prevalence of PCC by sociodemographics and prevalence of PCC symptoms. We fit multivariable log-binomial regression models to estimate the associations. Results The prevalence of PCC was 37.0%. The most common persistent symptoms were fatigue (56.8%), myalgia or arthralgia (47.5%), respiratory distress and dyspnea (42.7%), headache (34.0%), and cough (25.7%). The prevalence was higher in older people, women, and individuals with low socioeconomic status. There was no significant association between hypertension and PCC or diabetes and PCC prevalence. Conclusions About one-third of the adult Mexican population who had COVID-19 in 2021 had Post-COVID conditions. Our population-based estimates can help assess potential priorities for PCC-related health services, which is critical in light of our weak health system and limited funding.

Abstract COVID-19 vaccinations protect against severe illness and death, but associations with post-COVID conditions (PCC) are less clear. We aimed to evaluate the association between prior COVID-19 vaccination and new-onset PCC among individuals with SARS-CoV-2 infection across eight large healthcare systems in the United States. This retrospective matched cohort study used electronic health records (EHR) from patients with SARS-CoV-2 positive tests during March 2021-February 2022. Vaccinated and unvaccinated COVID-19 cases were matched on location, test date, severity of acute infection, age, and sex. Vaccination status was ascertained using EHR and integrated data on externally administered vaccines. Adjusted relative risks (RRs) were obtained from Poisson regression. PCC was defined as a new diagnosis in one of 13 PCC categories 30 days to 6 months following a positive SARS-CoV-2 test. The study included 161,531 vaccinated COVID-19 cases and 161,531 matched unvaccinated cases. Compared to unvaccinated cases, vaccinated cases had a similar or lower risk of all PCC categories except mental health disorders (RR: 1.06, 95% CI: 1.02–1.10). Vaccination was associated with ≥10% lower risk of sensory (RR: 0.90, 0.86–0.95), circulatory (RR: 0.88, 0.83–0.94), blood and hematologic (RR: 0.79, 0.71–0.89), skin and subcutaneous (RR: 0.69, 0.66–0.72), and non-specific COVID-19 related disorders (RR: 0.53, 0.51–0.56). In general, associations were stronger at younger ages but mostly persisted regardless of SARS-CoV-2 variant period, receipt of ≥3 vs. 1–2 vaccine doses, or time since vaccination. Pre-infection vaccination was associated with reduced risk of several PCC outcomes and hence may decrease the long-term consequences of COVID-19.

Abstract Background Symptoms of COVID-19 including fatigue and dyspnea, may persist for weeks to months after SARS-CoV-2 infection. This study compared self-reported disability among SARS-CoV-2-positive and negative persons with mild to moderate COVID-19-like illness who presented for outpatient care before widespread COVID-19 vaccination. Methods Unvaccinated adults with COVID-19-like illness enrolled within 10 days of illness onset at three US Flu Vaccine Effectiveness Network sites were tested for SARS-CoV-2 by molecular assay. Enrollees completed an enrollment questionnaire and two follow-up surveys (7–24 days and 2–7 months after illness onset) online or by phone to assess illness characteristics and health status. The second follow-up survey included questions measuring global health, physical function, fatigue, and dyspnea. Scores in the four domains were compared by participants’ SARS-CoV-2 test results in univariate analysis and multivariable Gamma regression. Results During September 22, 2020 – February 13, 2021, 2712 eligible adults were enrolled, 1541 completed the first follow-up survey, and 650 completed the second follow-up survey. SARS-CoV-2-positive participants were more likely to report fever at acute illness but were otherwise comparable to SARS-CoV-2-negative participants. At first follow-up, SARS-CoV-2-positive participants were less likely to have reported fully or mostly recovered from their illness compared to SARS-CoV-2-negative participants. At second follow-up, no differences by SARS-CoV-2 test results were detected in the four domains in the multivariable model. Conclusion Self-reported disability was similar among outpatient SARS-CoV-2-positive and -negative adults 2–7 months after illness onset.

Abstract Background An increasing number of studies have described new and persistent symptoms and conditions as potential post-acute sequelae of SARS-CoV-2 infection (PASC). However, it remains unclear whether certain symptoms or conditions occur more frequently among persons with SARS-CoV-2 infection compared with those never infected with SARS-CoV-2. We compared the occurrence of specific COVID-associated symptoms and conditions as potential PASC 31- to 150-day following a SARS-CoV-2 test among adults and children with positive and negative test results. Methods We conducted a retrospective cohort study using electronic health record (EHR) data from 43 PCORnet sites participating in a national COVID-19 surveillance program. This study included 3,091,580 adults (316,249 SARS-CoV-2 positive; 2,775,331 negative) and 675,643 children (62,131 positive; 613,512 negative) who had a SARS-CoV-2 laboratory test during March 1, 2020–May 31, 2021 documented in their EHR. We used logistic regression to calculate the odds of having a symptom and Cox models to calculate the risk of having a newly diagnosed condition associated with a SARS-CoV-2 positive test. Results After adjustment for baseline covariates, hospitalized adults and children with a positive test had increased odds of being diagnosed with ≥ 1 symptom (adults: adjusted odds ratio[aOR], 1.17[95% CI, 1.11–1.23]; children: aOR, 1.18[95% CI, 1.08–1.28]) or shortness of breath (adults: aOR, 1.50[95% CI, 1.38–1.63]; children: aOR, 1.40[95% CI, 1.15–1.70]) 31–150 days following a SARS-CoV-2 test compared with hospitalized individuals with a negative test. Hospitalized adults with a positive test also had increased odds of being diagnosed with ≥ 3 symptoms or fatigue compared with those testing negative. The risks of being newly diagnosed with type 1 or type 2 diabetes (adjusted hazard ratio[aHR], 1.25[95% CI, 1.17–1.33]), hematologic disorders (aHR, 1.19[95% CI, 1.11–1.28]), or respiratory disease (aHR, 1.44[95% CI, 1.30–1.60]) were higher among hospitalized adults with a positive test compared with those with a negative test. Non-hospitalized adults with a positive test also had higher odds or increased risk of being diagnosed with certain symptoms or conditions. Conclusions Patients with SARS-CoV-2 infection, especially those who were hospitalized, were at higher risk of being diagnosed with certain symptoms and conditions after acute infection.

Abstract Background Symptomatic COVID-19 and Long COVID, also referred to as post-acute sequelae of SARS-CoV-2 (PASC) or post-COVID conditions, have been widely reported in young, healthy people, but their prevalence has not yet been determined in student athletes. We sought to estimate the prevalence of reported COVID-19, symptomatic COVID-19, and Long COVID in college athletes in the United States attending 18 schools from spring 2020 to fall 2021. Methods We developed an online survey to measure the prevalence of student athletes who tested positive for COVID-19, developed Long COVID, and did not return to their sport during the relevant time period. We surveyed a convenience sample of 18 collegiate school administrators, representing about 7,000 student athletes. Of those schools surveyed, 16 responded regarding the spring 2020 semester, and 18 responded regarding the full academic year of fall 2020 to spring 2021 (both semesters). Results According to the survey responses, there were 9.8% of student athletes who tested positive for COVID-19 in spring 2020 and 25.4% who tested positive in the academic year of fall 2020 to spring 2021. About 4% of student athletes who tested positive from spring 2020 to spring 2021 developed Long COVID, defined as new, recurring, or ongoing physical or mental health consequences occurring 4 or more weeks after SARS-CoV-2 infection. Conclusions This study highlights that Long COVID occurs among young, healthy athletes and is a real consequence of COVID-19. Understanding the prevalence of Long COVID in this population requires longer follow-up and further study.

Introduction: Since March 2020, Hispanic and Black/African American persons have made up a disproportionate number of COVID-19 cases, hospitalizations, and deaths. However, little is known about whether the prevalence of postacute sequelae or post-COVID conditions differs by race/ethnicity. Methods: This study used cross-sectional survey data collected by Porter Novelli Public Services to determine the prevalence of ≥1 ongoing symptom lasting ≥4 weeks by SARS-CoV-2 test status and racial/ethnic groups among 2,890 adults in the U.S. Results: Overall, 57% (95% CI=54%, 60%) of respondents with positive SARS-CoV-2 tests reported ≥1 ongoing symptom, compared with 22% (95% CI=20%, 24%) of respondents who tested negative. Among those with positive SARS-CoV-2 tests, Hispanic respondents had higher AORs of experiencing ≥1 ongoing symptom (AOR=1.79, 95% CI=1.27, 2.53) than non-Hispanic White respondents. In addition, Hispanic respondents had significantly higher ORs of experiencing 2 or more ongoing symptoms (AOR=2.03, 95% CI=1.45, 2.86), respiratory/cardiac symptoms (AOR=1.47, 95% CI=1.03, 2.07), neurologic symptoms (AOR=1.77, 95% CI=1.26, 2.48), and other symptoms (AOR=1.53, 95% CI=1.09, 2.14) than non-Hispanic White respondents. Non-Hispanic other respondents who reported at least 1 positive SARS-CoV-2 test had significantly higher ORs of experiencing gastrointestinal symptoms (AOR=4.06, 95% CI=1.78, 8.89) than non-Hispanic White respondents. Conclusions: These results highlight potential disparities in ongoing symptoms, even after accounting for demographic differences, and reinforce the need for culturally appropriate and targeted strategies to increase access to health care and reduce SARS-CoV-2 infections.

IntroductionData on ethnic and racial differences in symptoms and health-related impacts following SARS-CoV-2 infection are limited. We aimed to estimate the ethnic and racial differences in symptoms and health-related impacts 3 and 6 months after the first SARS-CoV-2 infection.MethodsParticipants included adults with SARS-CoV-2 infection enrolled in a prospective multicenter US study between 12/11/2020 and 7/4/2022 as the primary cohort of interest, as well as a SARS-CoV-2-negative cohort to account for non-SARS-CoV-2-infection impacts, who completed enrollment and 3-month surveys (N = 3,161; 2,402 SARS-CoV-2-positive, 759 SARS-CoV-2-negative). Marginal odds ratios were estimated using GEE logistic regression for individual symptoms, health status, activity level, and missed work 3 and 6 months after COVID-19 illness, comparing each ethnicity or race to the referent group (non-Hispanic or white), adjusting for demographic factors, social determinants of health, substance use, pre-existing health conditions, SARS-CoV-2 infection status, COVID-19 vaccination status, and survey time point, with interactions between ethnicity or race and time point, ethnicity or race and SARS-CoV-2 infection status, and SARS-CoV-2 infection status and time point.ResultsFollowing SARS-CoV-2 infection, the majority of symptoms were similar over time between ethnic and racial groups. At 3 months, Hispanic participants were more likely than non-Hispanic participants to report fair/poor health (OR: 1.94; 95%CI: 1.36–2.78) and reduced activity (somewhat less, OR: 1.47; 95%CI: 1.06–2.02; much less, OR: 2.23; 95%CI: 1.38–3.61). At 6 months, differences by ethnicity were not present. At 3 months, Other/Multiple race participants were more likely than white participants to report fair/poor health (OR: 1.90; 95% CI: 1.25–2.88), reduced activity (somewhat less, OR: 1.72; 95%CI: 1.21–2.46; much less, OR: 2.08; 95%CI: 1.18–3.65). At 6 months, Asian participants were more likely than white participants to report fair/poor health (OR: 1.88; 95%CI: 1.13–3.12); Black participants reported more missed work (OR, 2.83; 95%CI: 1.60–5.00); and Other/Multiple race participants reported more fair/poor health (OR: 1.83; 95%CI: 1.10–3.05), reduced activity (somewhat less, OR: 1.60; 95%CI: 1.02–2.51; much less, OR: 2.49; 95%CI: 1.40–4.44), and more missed work (OR: 2.25; 95%CI: 1.27–3.98).DiscussionAwareness of ethnic and racial differences in outcomes following SARS-CoV-2 infection may inform clinical and public health efforts to advance health equity in long-term outcomes.

Abstract Background Although treatment and control of diabetes can prevent complications and reduce morbidity, few data sources exist at the state level for surveillance of diabetes comorbidities and control. Surveys and electronic health records (EHRs) offer different strengths and weaknesses for surveillance of diabetes and major metabolic comorbidities. Data from self-report surveys suffer from cognitive and recall biases, and generally cannot be used for surveillance of undiagnosed cases. EHR data are becoming more readily available, but pose particular challenges for population estimation since patients are not randomly selected, not everyone has the relevant biomarker measurements, and those included tend to cluster geographically. Methods We analyzed data from the National Health and Nutritional Examination Survey, the Health and Retirement Study, and EHR data from the DARTNet Institute to create state-level adjusted estimates of the prevalence and control of diabetes, and the prevalence and control of hypertension and high cholesterol in the diabetes population, age 50 and over for five states: Alabama, California, Florida, Louisiana, and Massachusetts. Results The estimates from the two surveys generally aligned well. The EHR data were consistent with the surveys for many measures, but yielded consistently lower estimates of undiagnosed diabetes prevalence, and identified somewhat fewer comorbidities in most states. Conclusions Despite these limitations, EHRs may be a promising source for diabetes surveillance and assessment of control as the datasets are large and created during the routine delivery of health care. Trial Registration: Not applicable.

ABSTRACT Wastewater-based epidemiology (WBE) emerged during the coronavirus disease 2019 (COVID-19) pandemic as a scalable and broadly applicable method for community-level monitoring of infectious disease burden. The lack of high-resolution fecal shedding data for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) limits our ability to link WBE measurements to disease burden. In this study, we present longitudinal, quantitative fecal shedding data for SARS-CoV-2 RNA, as well as for the commonly used fecal indicators pepper mild mottle virus (PMMoV) RNA and crAss-like phage (crAssphage) DNA. The shedding trajectories from 48 SARS-CoV-2-infected individuals suggest a highly individualized, dynamic course of SARS-CoV-2 RNA fecal shedding. Of the individuals that provided at least three stool samples spanning more than 14 days, 77% had one or more samples that tested positive for SARS-CoV-2 RNA. We detected PMMoV RNA in at least one sample from all individuals and in 96% (352/367) of samples overall. CrAssphage DNA was detected in at least one sample from 80% (38/48) of individuals and was detected in 48% (179/371) of all samples. The geometric mean concentrations of PMMoV and crAssphage in stool across all individuals were 8.7 × 104 and 1.4 × 104 gene copies/milligram-dry weight, respectively, and crAssphage shedding was more consistent for individuals than PMMoV shedding. These results provide us with a missing link needed to connect laboratory WBE results with mechanistic models, and this will aid in more accurate estimates of COVID-19 burden in sewersheds. Additionally, the PMMoV and crAssphage data are critical for evaluating their utility as fecal strength normalizing measures and for source-tracking applications. IMPORTANCE This research represents a critical step in the advancement of wastewater monitoring for public health. To date, mechanistic materials balance modeling of wastewater-based epidemiology has relied on SARS-CoV-2 fecal shedding estimates from small-scale clinical reports or meta-analyses of research using a wide range of analytical methodologies. Additionally, previous SARS-CoV-2 fecal shedding data have not contained sufficient methodological information for building accurate materials balance models. Like SARS-CoV-2, fecal shedding of PMMoV and crAssphage has been understudied to date. The data presented here provide externally valid and longitudinal fecal shedding data for SARS-CoV-2, PMMoV, and crAssphage which can be directly applied to WBE models and ultimately increase the utility of WBE.

Introduction Although SARS-CoV-2 vaccines were first approved under Emergency Use Authorization by the Food and Drug Administration in late 2020 for adults, authorisation for young children 6 months to 6 months and adults aged

Abstract Background A substantial portion of people with COVID-19 subsequently experience lasting symptoms including fatigue, shortness of breath, and neurological complaints such as cognitive dysfunction many months after acute infection. Emerging evidence suggests that this condition, commonly referred to as long COVID but also known as post-acute sequelae of SARS-CoV-2 infection (PASC) or post-COVID-19 condition, could become a significant global health burden. Main text While the number of studies investigating the post-COVID-19 condition is increasing, there is no agreement on how this new disease should be defined and diagnosed in clinical practice and what relevant outcomes to measure. There is an urgent need to optimise and standardise outcome measures for this important patient group both for clinical services and for research and to allow comparing and pooling of data. Conclusions A Core Outcome Set for post-COVID-19 condition should be developed in the shortest time frame possible, for improvement in data quality, harmonisation, and comparability between different geographical locations. We call for a global initiative, involving all relevant partners, including, but not limited to, healthcare professionals, researchers, methodologists, patients, and caregivers. We urge coordinated actions aiming to develop a Core Outcome Set (COS) for post-COVID-19 condition in both the adult and paediatric populations.

Abstract Background Disease surveillance of diabetes among youth has relied mainly upon manual chart review. However, increasingly available structured electronic health record (EHR) data have been shown to yield accurate determinations of diabetes status and type. Validated algorithms to determine date of diabetes diagnosis are lacking. The objective of this work is to validate two EHR-based algorithms to determine date of diagnosis of diabetes. Methods A rule-based ICD-10 algorithm identified youth with diabetes from structured EHR data over the period of 2009 through 2017 within three children’s hospitals that participate in the SEARCH for Diabetes in Youth Study: Cincinnati Children’s Hospital, Cincinnati, OH, Seattle Children’s Hospital, Seattle, WA, and Children’s Hospital Colorado, Denver, CO. Previous research and a multidisciplinary team informed the creation of two algorithms based upon structured EHR data to determine date of diagnosis among diabetes cases. An ICD-code algorithm was defined by the year of occurrence of a second ICD-9 or ICD-10 diabetes code. A multiple-criteria algorithm consisted of the year of first occurrence of any of the following: diabetes-related ICD code, elevated glucose, elevated HbA1c, or diabetes medication. We assessed algorithm performance by percent agreement with a gold standard date of diagnosis determined by chart review. Results Among 3777 cases, both algorithms demonstrated high agreement with true diagnosis year and differed in classification (p = 0.006): 86.5% agreement for the ICD code algorithm and 85.9% agreement for the multiple-criteria algorithm. Agreement was high for both type 1 and type 2 cases for the ICD code algorithm. Performance improved over time. Conclusions Year of occurrence of the second ICD diabetes-related code in the EHR yields an accurate diagnosis date within these pediatric hospital systems. This may lead to increased efficiency and sustainability of surveillance methods for incidence of diabetes among youth.

The impact of vaccination on SARS-CoV-2 infectiousness is not well understood. We compared longitudinal viral shedding dynamics in unvaccinated and fully vaccinated adults. SARS-CoV-2-infected adults were enrolled within 5 days of symptom onset and nasal specimens were self-collected daily for two weeks and intermittently for an additional two weeks. SARS-CoV-2 RNA load and infectious virus were analyzed relative to symptom onset stratified by vaccination status. We tested 1080 nasal specimens from 52 unvaccinated adults enrolled in the pre-Delta period and 32 fully vaccinated adults with predominantly Delta infections. While we observed no differences by vaccination status in maximum RNA levels, maximum infectious titers and the median duration of viral RNA shedding, the rate of decay from the maximum RNA load was faster among vaccinated; maximum infectious titers and maximum RNA levels were highly correlated. Furthermore, amongst participants with infectious virus, median duration of infectious virus detection was reduced from 7.5 days (IQR: 6.0-9.0) in unvaccinated participants to 6 days (IQR: 5.0-8.0) in those vaccinated (P = 0.02). Accordingly, the odds of shedding infectious virus from days 6 to 12 post-onset were lower among vaccinated participants than unvaccinated participants (OR 0.42 95% CI 0.19-0.89). These results indicate that vaccination had reduced the probability of shedding infectious virus after 5 days from symptom onset.

Abstract Aims To compare left ventricular structure (LV) and diastolic function in young adults with youth- onset diabetes by type, determine the prevalence of abnormal diastolic function by diabetes type using published values from age similar healthy controls, and examine the risk factors associated with diastolic function. Methods In a cross sectional analysis we compared LV structure and diastolic function from two dimensional echocardiogram in participants with type 1 (T1D) and type 2 diabetes (T2D) who participated in the SEARCH for Diabetes in Youth Study. Linear models were used to examine the risk factors associated with worse diastolic function. Results Of 479 participants studied, 258 had T1D (mean age 21.2 ± 5.2 years, 60.5% non-Hispanic white, 53.9% female) and 221 had T2D (mean age 24.8 ± 4.3 years, 24.4% non-Hispanic white, 73.8% female). Median diabetes duration was 11.6 years. Participants with T2D had greater LV mass index and worse diastolic function that persisted after adjustment for differences in risk factors compared with participants with T1D (all p

Wearing a facemask can help to decrease the transmission of COVID-19. We investigated self-reported mask use among subjects aged 18 years and older participating in the COVID-19 Community Research Partnership (CRP), a prospective longitudinal COVID-19 surveillance study in the mid-Atlantic and southeastern United States. We included those participants who completed ≥5 daily surveys each month from December 1, 2020 through August 31, 2021. Mask use was defined as self-reported use of a face mask or face covering on every interaction with others outside the household within a distance of less than 6 feet. Participants were considered vaccinated if they reported receiving ≥1 COVID-19 vaccine dose. Participants (n = 17,522) were 91% non-Hispanic White, 68% female, median age 57 years, 26% healthcare workers, with 95% self-reported receiving ≥1 COVID-19 vaccine dose through August 2021; mean daily survey response was 85%. Mask use was higher among vaccinated than unvaccinated participants across the study period, regardless of the month of the first dose. Mask use remained relatively stable from December 2020 through April (range 71–80% unvaccinated; 86–93% vaccinated) and declined in both groups beginning in mid-May 2021 to 34% and 42% respectively in June 2021; mask use increased again since July 2021. Mask use by all was lower during weekends and on Christmas and Easter, regardless of vaccination status. Independent predictors of higher mask use were vaccination, age ≥65 years, female sex, racial or ethnic minority group, and healthcare worker occupation, whereas a history of self-reported prior COVID-19 illness was associated with lower use.

Objective. Hyperglycemia early in the course of type 1 diabetes (T1D) may increase the risk of cardiometabolic complications later in life. We tested the hypothesis that there were temporal trends in population-level glycemia and insulin pump use near T1D diagnosis among incident youth cohorts diagnosed between 2002 and 2016. Methods. Weighted and adjusted regression models were applied to data from the SEARCH for Diabetes in Youth study to analyze trends in hemoglobin A1c (HbA1c), suboptimal glycemia (HbA1c>9% or not), and insulin pump use among youth with T1D within 30 months of diagnosis. We tested the interaction of year with race and ethnicity, sex, and insulin regimen to assess potential disparities. Results. Among the 3,956 youth with T1D, there was a small, clinically insignificant reduction in HbA1c between 2002 (7.9%±1.5) and 2016 (7.8%±2.4) (fully adjusted change by year (-0.013% [95% CI -0.026, -0.0008], p=0.04). The proportion of youth with suboptimal glycemia increased with each year, but the adjusted odds did not change. Insulin pump use increased more than fivefold. Although interaction effects of time with race and ethnicity, sex, and insulin regimen were not detected, in 2016, suboptimal glycemia was 4.3 and 1.8 times more prevalent among Black and Hispanic than among non-Hispanic White youth, respectively. Conclusions. There was not a clinically significant population-level improvement in glycemia across incident youth cohorts early in the course of T1D, despite severalfold increases in insulin pump use. Comprehensive clinical interventions to improve glycemia early in the T1D course and address disparities are urgently needed.

Background Reports on medium and long-term sequelae of SARS-CoV-2 infections largely lack quantification of incidence and relative risk. We describe the rationale and methods of the Innovative Support for Patients with SARS-CoV-2 Registry (INSPIRE) that combines patient-reported outcomes with data from digital health records to understand predictors and impacts of SARS-CoV-2 infection. Methods INSPIRE is a prospective, multicenter, longitudinal study of individuals with symptoms of SARS-CoV-2 infection in eight regions across the US. Adults are eligible for enrollment if they are fluent in English or Spanish, reported symptoms suggestive of acute SARS-CoV-2 infection, and if they are within 42 days of having a SARS-CoV-2 viral test (i.e., nucleic acid amplification test or antigen test), regardless of test results. Recruitment occurs in-person, by phone or email, and through online advertisement. A secure online platform is used to facilitate the collation of consent-related materials, digital health records, and responses to self-administered surveys. Participants are followed for up to 18 months, with patient-reported outcomes collected every three months via survey and linked to concurrent digital health data; follow-up includes no in-person involvement. Our planned enrollment is 4,800 participants, including 2,400 SARS-CoV-2 positive and 2,400 SARS-CoV-2 negative participants (as a concurrent comparison group). These data will allow assessment of longitudinal outcomes from SARS-CoV-2 infection and comparison of the relative risk of outcomes in individuals with and without infection. Patient-reported outcomes include self-reported health function and status, as well as clinical outcomes including health system encounters and new diagnoses. Results Participating sites obtained institutional review board approval. Enrollment and follow-up are ongoing. Conclusions This study will characterize medium and long-term sequelae of SARS-CoV-2 infection among a diverse population, predictors of sequelae, and their relative risk compared to persons with similar symptomatology but without SARS-CoV-2 infection. These data may inform clinical interventions for individuals with sequelae of SARS-CoV-2 infection.

Objectives By race/ethnicity and socioeconomic position (SEP), to estimate and examine changes over time in (1) mortality rate, (2) mortality disparities and (3) excess mortality risk attributed to diagnosed diabetes (DM).Design Population-based cohort study using National Health Interview Survey data linked to mortality status from the National Death Index from survey year up to 31 December 2015 with 5 years person-time.Participants US adults aged ≥25 years with (31 586) and without (332 451) DM.Primary outcome Age-adjusted all-cause mortality rate for US adults with DM in each subgroup of SEP (education attainment and income-to-poverty ratio (IPR)) and time (1997–2001, 2002–2006 and 2007–2011).Results Among adults with DM, mortality rates fell from 23.5/1000 person-years (p-y) in 1997–2001 to 18.1/1000 p-y in 2007–2011 with changes of −5.2/1000 p-y for non-Hispanic whites; −5.2/1000 p-y for non-Hispanic blacks; and −5.4/1000 p-y for Hispanics. Rates significantly declined within SEP groups, measured as education attainment (high school=−4.8/1000 p-y) and IPR group (poor=−7.9/1000 p-y; middle income=−4.7/1000 p-y; and high income=−6.2/1000 p-y; but not for near poor). For adults with DM, statistically significant all-cause mortality disparity showed greater mortality rates for the lowest than the highest SEP level (education attainment and IPR) in each time period. However, patterns in mortality trends and disparity varied by race/ethnicity. The excess mortality risk attributed to DM significantly decreased from 1997–2001 to 2007–2011, within SEP levels, and among Hispanics and non-Hispanic whites; but no statistically significant changes among non-Hispanic blacks.Conclusions There were substantial improvements in all-cause mortality among US adults. However, we observed SEP disparities in mortality across race/ethnic groups or for adults with and without DM despite targeted efforts to improve access and quality of care among disproportionately affected populations.

Objectives: Surveys for U.S. diabetes surveillance do not reliably distinguish between type 1 and type 2 diabetes, potentially obscuring trends in type 1 among adults. To validate survey-based algorithms for distinguishing diabetes type, we linked survey data collected from adult patients with diabetes to a gold standard diabetes type. Research design and methods: We collected data through a telephone survey of 771 adults with diabetes receiving care in a large healthcare system in North Carolina. We tested 34 survey classification algorithms utilizing information on respondents’ report of physician-diagnosed diabetes type, age at onset, diabetes drug use, and body mass index. Algorithms were evaluated by calculating type 1 and type 2 sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to a gold standard diagnosis of diabetes type determined through analysis of EHR data and endocrinologist review of selected cases. Results: Algorithms based on self-reported type outperformed those based solely on other data elements. The top-performing algorithm classified as type 1 all respondents who reported type 1 and were prescribed insulin, as “other diabetes type” all respondents who reported “other,” and as type 2 the remaining respondents (type 1 sensitivity 91.6%, type 1 specificity 98.9%, type 1 PPV 82.5%, type 1 NPV 99.5%). This algorithm performed well in most demographic subpopulations. Conclusions: The major federal health surveys should consider including self-reported diabetes type if they do not already, as the gains in the accuracy of typing are substantial compared to classifications based on other data elements. This study provides much-needed guidance on the accuracy of survey-based diabetes typing algorithms.

BACKGROUND:Considerable geographic variation exists in the prevalence of chronic kidney disease across the United States. While some of this variability can be explained by differences in patient-level risk factors, substantial variability still exists. We hypothesize this may be due to understudied environmental exposures such as air pollution. METHODS:Using data on 1.1 million persons from the 2010 5% Medicare sample and Environmental Protection Agency air-quality measures, we examined the association between county-level particulate matter ≤2.5 μm (PM2.5) and the prevalence of diagnosed CKD, based on claims. Modified Poisson regression was used to estimate associations (prevalence ratios [PR]) between county PM2.5 concentration and individual-level diagnosis of CKD, adjusting for age, sex, race/ethnicity, hypertension, diabetes, and urban/rural status. RESULTS:Prevalence of diagnosed CKD ranged from 0% to 60% by county (median = 16%). As a continuous variable, PM2.5 concentration shows adjusted PR of diagnosed CKD = 1.03 (95% CI: 1.02-1.05; p