Serious morbidity may elevate nutrient requirements and affect adherence to feeding guidelines for very low birth weight (VLBW) infants. An understanding of factors affecting nutrient intakes of VLBW infants will facilitate development of strategies to improve nutrient provision. Our aim was to examine the impact of neonatal morbidity count on achieving recommended nutrient intakes in VLBW infants.VLBW infants enrolled in the Donor Milk for Improved Neurodevelopmental Outcomes trial (ISRCTN35317141, n = 363) were included. Serious morbidities and daily parenteral and enteral intakes were collected prospectively.Median intakes of infants with and without ≥1 morbidity met protein recommendations (3.5-4.5 g/kg/d) by week 2, although not maintained after week 4. Infants with ≥1 morbidity (vs without) were 2 weeks slower in achieving lipid (4.8-6.6 g/kg/d; week 4 vs 2) and energy (110-130 kcal/kg/d; week 5 vs 3) and 1 week slower in achieving carbohydrate recommendations (11.6-13.2 g/kg/d; week 4 vs 3). Adjusted hazard ratios of first achieving recommendations on any given day in infants with any 1 or 2 morbidities were 0.6 (95% confidence interval [CI], 0.5-0.9) and 0.6 (0.4-0.9), respectively, for protein; 0.5 (0.4-0.7) and 0.3 (0.2-0.5) for lipid; and 0.5 (0.4-0.7) and 0.3 (0.2-0.4) for energy.Morbidity is associated with a decreased likelihood of achieving lipid and consequently energy recommendations. This and the decline in protein intakes after the early neonatal period require further investigation to ensure optimal nutrition in this vulnerable population.
Abstract Objectives To estimate the impact of the COVID-19 pandemic on cardiovascular disease (CVD) and CVD management using routinely collected medication data as a proxy. Design Descriptive and interrupted time series analysis using anonymised individual-level population-scale data for 1.32 billion records of dispensed CVD medications across 15.8 million individuals in England, Scotland and Wales. Setting Community dispensed CVD medications with 100% coverage from England, Scotland and Wales, plus primary care prescribed CVD medications from England (including 98% English general practices). Participants 15.8 million individuals aged 18+ years alive on 1 st April 2018 dispensed at least one CVD medicine in a year from England, Scotland and Wales. Main outcome measures Monthly counts, percent annual change (1 st April 2018 to 31 st July 2021) and annual rates (1 st March 2018 to 28 th February 2021) of medicines dispensed by CVD/ CVD risk factor; prevalent and incident use. Results Year-on-year change in dispensed CVD medicines by month were observed, with notable uplifts ahead of the first (11.8% higher in March 2020) but not subsequent national lockdowns. Using hypertension as one example of the indirect impact of the pandemic, we observed 491,203 fewer individuals initiated antihypertensive treatment across England, Scotland and Wales during the period March 2020 to end May 2021 than would have been expected compared to 2019. We estimated that this missed antihypertension treatment could result in 13,659 additional CVD events should individuals remain untreated, including 2,281 additional myocardial infarctions (MIs) and 3,474 additional strokes. Incident use of lipid-lowering medicines decreased by an average 14,793 per month in early 2021 compared with the equivalent months prior to the pandemic in 2019. In contrast, the use of incident medicines to treat type-2 diabetes (T2DM) increased by approximately 1,642 patients per month. Conclusions Management of key CVD risk factors as proxied by incident use of CVD medicines has not returned to pre-pandemic levels in the UK. Novel methods to identify and treat individuals who have missed treatment are urgently required to avoid large numbers of additional future CVD events, further adding indirect cost of the COVID-19 pandemic.
Background Optimal in‐hospital growth is associated with improved long‐term health and neurodevelopment in VLBW infants. Previous studies have examined many explanatory factors (e.g. birth characteristics, type of enteral feeding, nutrient intakes, and morbidity). However, few studies have systematically evaluated these factors together to examine growth rates over the entire in‐hospital course. Objective To examine associations between infant characteristics, morbidities, daily macronutrient and energy intakes, and in‐hospital weight, length, and head circumference (HC) trajectories over time. Methods 316 VLBW infants from the GTA‐DoMINO (ISRCTN35317141) trial were included. Infant characteristics, morbidities, daily nutrient intakes, and weekly anthropometrics were collected prospectively from birth to 90‐days or until hospital discharge, whichever occurred first. Repeated measures linear regression models were used to assess the relationship between explanatory factors and anthropometric measures over time. Results Mean birth weight, length, and HC were 1021 (SD, 260) g, 35.7 (3.4) cm, and 25.1 (2.3) cm, respectively. At birth, mean gestational age was 28.0 (2.5) weeks and 13% of infants were small for gestational age. All macronutrient and energy intakes were associated with weight over time. Of energy and macronutrients, energy intakes showed the greatest association with mean weight gain during postnatal days 9–29 (2.5 g/kg/d, p<.0001); however, lipid intakes showed the greatest association with weight gain during days 30–90 (3.0 g/kg/d, p<.0001). From days 1–8, all macronutrient and energy intakes were associated with length over time (p‐values: 0.003–0.0005); however, no intakes were associated with length beyond day 8. Lipid and energy intakes were associated with HC over time during days 1–8 and 9–29 (p‐values: 0.01–0.007), but not beyond the first month. Infants fed >50% donor milk showed slower weight gain between days 9–29 compared to infants fed >50% formula (−0.9 g/kg/d) or mothers' milk (−1.1 g/kg/d, p<.0001); while the HC gains of infants fed >50% donor milk and mothers' milk were similar, they were slower than the HC gains of infants fed >50% formula. Of morbidities assessed, patent ductus arteriosus showed the greatest association with weight (−2.6 g/kg/d, p<.0001) and HC (−0.21 cm/wk, p<.0001) during days 9–29, while late‐onset sepsis showed the greatest association with weight (−1.6 g/kg/d, p<.0001) and length (−0.13 cm/wk, p<.0006) during days 30–90. Conclusions Daily macronutrient and energy intakes are associated with weight gain over time, regardless of postnatal age. Early, rather than late, nutrient intakes are associated with length and HC gains over time. Morbidities and the strength of their association with growth are dependent upon the infants' postnatal age during hospitalization. Support or Funding Information Funded by CIHR (MOP#102638; FDN#143233).
Objectives To use national, pre- and post-pandemic electronic health records (EHR) to develop and validate a scenario-based model incorporating baseline mortality risk, infection rate (IR) and relative risk (RR) of death for prediction of excess deaths. Design An EHR-based, retrospective cohort study. Setting Linked EHR in Clinical Practice Research Datalink (CPRD); and linked EHR and COVID-19 data in England provided in NHS Digital Trusted Research Environment (TRE). Participants In the development (CPRD) and validation (TRE) cohorts, we included 3.8 million and 35.1 million individuals aged ≥30 years, respectively. Main outcome measures One-year all-cause excess deaths related to COVID-19 from March 2020 to March 2021. Results From 1 March 2020 to 1 March 2021, there were 127,020 observed excess deaths. Observed RR was 4.34% (95% CI, 4.31–4.38) and IR was 6.27% (95% CI, 6.26–6.28). In the validation cohort, predicted one-year excess deaths were 100,338 compared with the observed 127,020 deaths with a ratio of predicted to observed excess deaths of 0.79. Conclusions We show that a simple, parsimonious model incorporating baseline mortality risk, one-year IR and RR of the pandemic can be used for scenario-based prediction of excess deaths in the early stages of a pandemic. Our analyses show that EHR could inform pandemic planning and surveillance, despite limited use in emergency preparedness to date. Although infection dynamics are important in the prediction of mortality, future models should take greater account of underlying conditions.
Social workers have played an integral role in society's response to the HIV/AIDS pandemic since the discovery of the disease. As the landscape of the epidemic has changed, so has the social work response to it. Social workers are, and have been, central to the success of TESTAZ (Test, Educate, Support, and Treat Arizona), which is a nontargeted, routine opt-out HIV screening program in the emergency department (ED) of Maricopa Medical Center. This article focuses on the crucial role social workers play in every stage of program development, implementation, and patient movement through the stages of the HIV care continuum. Social worker involvement with HIV-positive patients diagnosed in the ED is imperative to achieving patient viral suppression.
Abstract Background Emerging data-driven technologies in healthcare, such as risk prediction models, hold great promise but also pose challenges regarding potential bias and exacerbation of existing health inequalities, which have been observed across diseases such as cardiovascular disease (CVD) and COVID-19. This study addresses the impact of ethnicity in risk prediction modelling for cardiovascular events following SARS-CoV-2 infection and explores the potential of ethnicity-specific models to mitigate disparities. Methods This retrospective cohort study utilises six linked datasets accessed through National Health Service (NHS) England’s Secure Data Environment (SDE) service for England, via the BHF Data Science Centre’s CVD-COVID-UK/COVID-IMPACT Consortium. Inclusion criteria were established, and demographic information, risk factors, and ethnicity categories were defined. Four feature selection methods (LASSO, Random Forest, XGBoost, QRISK) were employed and ethnicity-specific prediction models were trained and tested using logistic regression. Discrimination (AUROC) and calibration performance were assessed for different populations and ethnicity groups. Findings Several differences were observed in the models trained on the whole study cohort vs ethnicity-specific groups. At the feature selection stage, ethnicity-specific models yielded different selected features. AUROC discrimination measures showed consistent performance across most ethnicity groups, with QRISK-based models performing relatively poorly. Calibration performance exhibited variation across ethnicity groups and age categories. Ethnicity-specific models demonstrated the potential to enhance calibration performance for certain ethnic groups. Interpretation This research highlights the importance of considering ethnicity in risk prediction modelling to ensure equitable healthcare outcomes. Differences in selected features and asymmetric calibration across ethnicities underscore the necessity of tailored approaches. Ethnicity-specific models offer a pathway to addressing disparities and improving model performance. The study emphasises the role of data-driven technologies in either alleviating or exacerbating existing health inequalities. Evidence before this study Research has suggested that SARS-CoV-2 infections may have prognostic value in predicting later cardiovascular disease outcomes, two diseases where ethnicity-based health inequalities have been observed. Existing health inequalities are at risk of being exacerbated by bias in emerging data-driven technologies such as risk prediction models, and there currently exists no recommended practice to mitigate this issue. Model performances are not typically stratified by ethnic groups and, if reported, ethnic groups are often only included in higher-level categories that have been criticised for simplicity of definition and for missing key ethnic heterogeneity. Added value of this study This study demonstrates the impact of including an in-depth consideration of ethnicity and its granularity in risk prediction modelling for cardiovascular event prediction in patients following a SARS-CoV-2 infection. This is one of, if not the first, set of models specifically built for and representative of all ethnic groups across an entire population, evaluating different practices to best mitigate ethnicity-based disparities in prediction algorithms. Moreover, ethnicity data has historically not been well captured, with as many as 1 in 3 individuals missing ethnicity data in their health records. With data linkage, this work is the first to analyse 96% complete ethnicity records in one of the world’s largest ethnically diverse routinely collected datasets. Implications of all of the available evidence This study highlights the potential of tailoring feature selection, performance measures, and probability scores to different ethnic groups through ethnicity-specific risk prediction models to mitigate prediction bias. We identify differences between models trained on the global study populations to cohorts of specific ethnicities, and encourage the use of more granular ethnicity categories to capture the diversity of underlying populations. Such approaches will allow for newly developed data-driven tools to cater to the ethnic heterogeneity present between populations and ensure that emerging technologies translate into equitable health outcomes for everyone.
Abstract Health care in the United States is changing, and diagnostic radiology is attempting to adapt to the new norm. A view of the landscape shows mergers, acquisitions, and radiology practices becoming larger. Musculoskeletal (MSK) radiology is trending toward subspecialization, and orthopaedic surgery practices are demanding quality, convenience, and efficiency in imaging services. In other industries, optimization of operations and strategic deployment of resources are standard, but radiology is not quite there yet. This article details our opportunities in MSK imaging to increase market share through service, added value, and improved operational efficiency.
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