Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be integrated with COVID-19 case data to inform timely pandemic response. However, more research is needed to apply and develop systematic methods to interpret the true SARS-CoV-2 signal from noise introduced in wastewater samples (e.g., from sewer conditions, sampling and extraction methods, etc.). In this study, raw wastewater was collected weekly from five sewersheds and one residential facility. The concentrations of SARS-CoV-2 in wastewater samples were compared to geocoded COVID-19 clinical testing data. SARS-CoV-2 was reliably detected (95% positivity) in frozen wastewater samples when reported daily new COVID-19 cases were 2.4 or more per 100,000 people. To adjust for variation in sample fecal content, four normalization biomarkers were evaluated: crAssphage, pepper mild mottle virus, Bacteroides ribosomal RNA (rRNA), and human 18S rRNA. Of these, crAssphage displayed the least spatial and temporal variability. Both unnormalized SARS-CoV-2 RNA signal and signal normalized to crAssphage had positive and significant correlation with clinical testing data (Kendall's Tau-b (τ)=0.43 and 0.38, respectively), but no normalization biomarker strengthened the correlation with clinical testing data. Locational dependencies and the date associated with testing data impacted the lead time of wastewater for clinical trends, and no lead time was observed when the sample collection date (versus the result date) was used for both wastewater and clinical testing data. This study supports that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence and presents tools that could be applied to make wastewater signal more interpretable and comparable across studies.

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be integrated with COVID-19 case data to inform timely pandemic response. However, more research is needed to apply and develop systematic methods to interpret the true SARS-CoV-2 signal from noise introduced in wastewater samples (e.g., from sewer conditions, sampling and extraction methods, etc.). In this study, raw wastewater was collected weekly from five sewersheds and one residential facility, and wastewater SARS-CoV-2 concentrations were compared to geocoded COVID-19 clinical testing data. SARS-CoV-2 was reliably detected (95% positivity) in frozen wastewater samples when reported daily new COVID-19 cases were 2.4 or more per 100,000 people. To adjust for variation in sample fecal content, crAssphage, pepper mild mottle virus, Bacteroides ribosomal RNA (rRNA), and human 18S rRNA were evaluated as normalization biomarkers, and crAssphage displayed the least spatial and temporal variability. Both unnormalized SARS-CoV-2 RNA signal and signal normalized to crAssphage had positive and significant correlation with clinical testing data (Kendall's Tau-b=0.43 and 0.38, respectively). Locational dependencies and the date associated with testing data impacted the lead time of wastewater for clinical trends, and no lead time was observed when the sample collection date (versus the result date) was used for both wastewater and clinical testing data. This study supports that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence and presents approaches that could be applied to make wastewater signal more interpretable and comparable across studies.

Background Gestational diabetes (GDM) is prevalent and benefits from timely and effective treatment, given the short window to impact glycemic control. Clinicians face major barriers to choosing effectively among treatment modalities [medical nutrition therapy (MNT) with or without pharmacologic treatment (antidiabetic oral agents and/or insulin)]. We investigated whether clinical data at varied stages of pregnancy can predict GDM treatment modality. Methods Among a population-based cohort of 30,474 pregnancies with GDM delivered at Kaiser Permanente Northern California in 2007–2017, we selected those in 2007–2016 as the discovery set and 2017 as the temporal/future validation set. Potential predictors were extracted from electronic health records at different timepoints (levels 1–4): (1) 1-year preconception to the last menstrual period, (2) the last menstrual period to GDM diagnosis, (3) at GDM diagnosis, and (4) 1 week after GDM diagnosis. We compared transparent and ensemble machine learning prediction methods, including least absolute shrinkage and selection operator (LASSO) regression and super learner, containing classification and regression tree, LASSO regression, random forest, and extreme gradient boosting algorithms, to predict risks for pharmacologic treatment beyond MNT. Results The super learner using levels 1–4 predictors had higher predictability [tenfold cross-validated C-statistic in discovery/validation set: 0.934 (95% CI: 0.931–0.936)/0.815 (0.800–0.829)], compared to levels 1, 1–2, and 1–3 (discovery/validation set C-statistic: 0.683–0.869/0.634–0.754). A simpler, more interpretable model, including timing of GDM diagnosis, diagnostic fasting glucose value, and the status and frequency of glycemic control at fasting during one-week post diagnosis, was developed using tenfold cross-validated logistic regression based on super learner-selected predictors. This model compared to the super learner had only a modest reduction in predictability [discovery/validation set C-statistic: 0.825 (0.820–0.830)/0.798 (95% CI: 0.783–0.813)]. Conclusions Clinical data demonstrated reasonably high predictability for GDM treatment modality at the time of GDM diagnosis and high predictability at 1-week post GDM diagnosis. These population-based, clinically oriented models may support algorithm-based risk-stratification for treatment modality, inform timely treatment, and catalyze more effective management of GDM.

ObjectiveHousehold food insufficiency (HFIS) is a major public health threat to children. Children may be particularly vulnerable to HFIS as a psychological stressor due to their rapid growth and accelerated behavioural and cognitive states, whereas data focusing on HFIS and childhood mental disorders are as-yet sparse. We aimed to examine the associations of HFIS with depression and anxiety in US children.DesignCross-sectional study.SettingThe 2016–2018 National Survey of Children’s Health, a nationally-representative study.ParticipantsPrimary caregivers of 102 341 children in the USA.Primary and secondary outcome measuresPhysician diagnosed depression and anxiety were assessed by questionnaires administered to primary caregivers of 102 341 children. Multivariable logistic regression models estimated adjusted OR (aOR) for current depression or anxiety associated with HFIS measured through a validated single-item instrument.ResultsAmong children aged 3–17 years, 3.2% and 7.4% had parent-reported physician-diagnosed current depression and anxiety, respectively. Compared with children without HFIS, children with HFIS had approximately twofold higher weighted prevalence of anxiety or depression. After adjusting for covariates, children with versus without HFIS had a 1.53-fold (95% CI 1.15 to 2.03) and 1.48-fold (95% CI 1.20 to 1.82) increased odds of current depression and anxiety, respectively. Associations were slightly more pronounced among girls (aOR (95% CI): depression 1.69 (1.16 to 2.48); anxiety 1.78 (1.33 to 2.38)) than boys (1.42 (0.98 to 2.08); 1.32 (1.00 to 1.73); both P-for-interaction <0.01). The associations did not vary by children’s age or race/ethnicity.ConclusionsHFIS was independently associated with depression and anxiety among US children. Girls presented slightly greater vulnerability to HFIS in terms of impaired mental health. Children identified as food-insufficient may warrant mental health assessment and possible intervention. Assessment of HFIS among children with impaired mental health is also warranted. Our findings also highlight the importance of promptly addressing HFIS with referral to appropriate resources and inform its potential to alleviate childhood mental health issues.