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We examined whether change in added sugar intake is associated with change in δ13C, a novel sugar biomarker, in thirty-nine children aged 5–10 years selected from a Colorado (USA) prospective cohort of children at increased risk for type 1 diabetes. Reported added sugar intake via FFQ and δ13C in erythrocytes were measured at two time points a median of 2 years apart. Change in added sugar intake was associated with change in the δ13C biomarker, where for every 1-g increase in added sugar intake between the two time points, there was an increase in δ13C of 0⋅0082 (P = 0⋅0053), independent of change in HbA1c and δ15N. The δ13C biomarker may be used as a measure of compliance in an intervention study of children under the age of 10 years who are at increased risk for type 1 diabetes, in which the goal was to reduce dietary sugar intake. [ABSTRACT FROM AUTHOR]

Objectives. We assessed whether living in counties with Title X clinics and increased use of long-acting reversible contraception (LARC) in Colorado are associated with decreased risk of adverse birth outcomes. Methods. We linked Title X clinic counties to the Colorado birth data set by using the mother's county of residence. We compared low birth weight (LBW) and preterm birth (PTB) in 2008 and 2012, in counties with and without Title X clinics. We compared the relationship between LARC use and the incidence of LBW or PTB in 2012 for women living in counties with Title X clinics. Results. For women living in counties with Title X clinics, the odds of PTB were significantly lower in 2012 compared with 2008 (odds ratio = 0.85; 95% confidence interval = 0.81, 0.89; interaction P=.02). For women living in Title X clinic counties in 2012, a higher proportion of LARC use (>12.4%) was significantly associated with decreased risk of PTB (P= .02) compared with a low proportion of LARC use (£4.96%). Conclusions. Improved access to family planning services and increased use of LARC are associated with lower risk of PTB. [ABSTRACT FROM AUTHOR]

Over 6.37 million people have died from COVID-19 worldwide, but factors influencing COVID-19-related mortality remain understudied. We aimed to describe and identify risk factors for COVID-19 mortality in the Colorado Center for Personalized Medicine (CCPM) Biobank using integrated data sources, including Electronic Health Records (EHRs). We calculated cause-specific mortality and case-fatality rates for COVID-19 and common pre-existing health conditions defined by diagnostic phecodes and encounters in EHRs. We performed multivariable logistic regression analyses of the association between each pre-existing condition and COVID-19 mortality. Of the 155,859 Biobank participants enrolled as of July 2022, 20,797 had been diagnosed with COVID-19. Of 5334 Biobank participants who had died, 190 were attributed to COVID-19. The case-fatality rate was 0.91% and the COVID-19 mortality rate was 122 per 100,000 persons. The odds of dying from COVID-19 were significantly increased among older men, and those with 14 of the 61 pre-existing conditions tested, including hypertensive chronic kidney disease (OR: 10.14, 95% CI: 5.48, 19.16) and type 2 diabetes with renal manifestations (OR: 5.59, 95% CI: 3.42, 8.97). Male patients who are older and have pre-existing kidney diseases may be at higher risk for death from COVID-19 and may require special care.

Background: Characterizing the experience and impact of the COVID-19 pandemic among various populations remains challenging due to the limitations inherent in common data sources, such as electronic health records (EHRs) or cross-sectional surveys., Objective: This study aims to describe testing behaviors, symptoms, impact, vaccination status, and case ascertainment during the COVID-19 pandemic using integrated data sources., Methods: In summer 2020 and 2021, we surveyed participants enrolled in the Biobank at the Colorado Center for Personalized Medicine (CCPM; N=180,599) about their experience with COVID-19. The prevalence of testing, symptoms, and impacts of COVID-19 on employment, family life, and physical and mental health were calculated overall and by demographic categories. Survey respondents who reported receiving a positive COVID-19 test result were considered a "confirmed case" of COVID-19. Using EHRs, we compared COVID-19 case ascertainment and characteristics in EHRs versus the survey. Positive cases were identified in EHRs using the International Statistical Classification of Diseases, 10th revision (ICD-10) diagnosis codes, health care encounter types, and encounter primary diagnoses., Results: Of the 25,063 (13.9%) survey respondents, 10,661 (42.5%) had been tested for COVID-19, and of those, 1366 (12.8%) tested positive. Nearly half of those tested had symptoms or had been exposed to someone who was infected. Young adults (18-29 years) and Hispanics were more likely to have positive tests compared to older adults and persons of other racial/ethnic groups. Mental health (n=13,688, 54.6%) and family life (n=12,233, 48.8%) were most negatively affected by the pandemic and more so among younger groups and women; negative impacts on employment were more commonly reported among Black respondents. Of the 10,249 individuals who responded to vaccination questions from version 2 of the survey (summer 2021), 9770 (95.3%) had received the vaccine. After integration with EHR data up to the time of the survey completion, 1006 (4%) of the survey respondents had a discordant COVID-19 case status between EHRs and the survey. Using all longitudinal EHR and survey data, we identified 11,472 (6.4%) COVID-19-positive cases among Biobank participants. In comparison to COVID-19 cases identified through the survey, EHR-identified cases were younger and more likely to be Hispanic., Conclusions: We found that the COVID-19 pandemic has had far-reaching and varying effects among our Biobank participants. Integrated data assets, such as the Biobank at the CCPM, are key resources for population health monitoring in response to public health emergencies, such as the COVID-19 pandemic., (©Randi K Johnson, Katie M Marker, David Mayer, Jonathan Shortt, David Kao, Kathleen C Barnes, Jan T Lowery, Christopher R Gignoux. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 13.06.2022.)