Breast milk versus Formula: What\u27s the Big deal?

Breastfeeding versus formula feeding infants has long been a debate and question for parents. Many negative and positive points are brought up for each side of the debate, but an overwhelming majority of evidence has pushed for breastfeeding and its positive benefits for the newborn. However, there is a concern for the incidence of hyperbilirubinemia in infants and whether breastfeeding or formula feeding affects bilirubin levels. The aim of this project is to gather evidence to determine whether or not bilirubin levels in newborns are significantly influenced in the first one to three weeks of life by source of feeding, either breast feeding or formula feeding. Our evidence was gathered from the following accessed databases: Medline, Proquest, and CINAHL. Our findings did suggest that bilirubin levels are higher in breastfed newborns in the first week to three weeks of life, but the positive benefits of breastfeeding out weighted the cons; however, great caution should be taken to monitor the levels of bilirubin in all infants due to the risk of hyperbilirubinemia causing neurological harm, such as brain damage. Therefore, nurses and all medical professionals working with infants should practice effective monitoring of bilirubin levels in newborns to effectively intervene when bilirubin levels reach a dangerous level and provide sufficient education to the parents

Effects of Restoration of Blood Flow on the Development of Aortic Atherosclerosis in ApoE −/− Mice With Unilateral Renal Artery Stenosis

BACKGROUND: Chronic unilateral renal artery stenosis (RAS) causes accelerated atherosclerosis in apolipoprotein E-deficient (ApoE(-/-)) mice, but effects of restoration of renal blood flow on aortic atherosclerosis are unknown. METHODS AND RESULTS: Male ApoE(-/-) mice underwent sham surgery (n=16) or had partial ligation of the right renal artery (n=41) with the ligature being removed 4 days later (D4LR; n=6), 8 days later (D8LR; n=11), or left in place for 90 days (chronic RAS; n=24). Ligature removal at 4 or 8 days resulted in improved renal blood flow, decreased plasma angiotensin II levels, a return of systolic blood pressure to baseline, and increased plasma levels of neutrophil gelatinase associated lipocalin. Chronic RAS resulted in increased lipid staining in the aortic arch (33.2% [24.4, 47.5] vs 11.6% [6.1, 14.2]; P<0.05) and descending thoracic aorta (10.2% [6.4, 25.9] vs 4.9% [2.8, 7.8]; P<0.05), compared to sham surgery. There was an increased amount of aortic arch lipid staining in the D8LR group (22.7% [22.1, 32.7]), compared to sham-surgery, but less than observed with chronic RAS. Lipid staining in the aortic arch was not increased in the D4LR group, and lipid staining in the descending aorta was not increased in either the D8LR or D4LR groups. There was less macrophage expression in infrarenal aortic atheroma in the D4LR and D8LR groups compared to the chronic RAS group. CONCLUSIONS: Restoration of renal blood flow at either 4 or 8 days after unilateral RAS had a beneficial effect on systolic blood pressure, aortic lipid deposition, and atheroma inflammation

Community Justice and Public Safety: Assessing Criminal Justice Policy Through the Lens of the Social Contract

A reconceptualization of the idea of “community justice” is framed in the logic of the social contract and emphasizes the responsibility of the justice system for the provision of public safety. First, we illustrate the ways in which the criminal justice system has hindered the efforts of community residents to participate in the production of public safety by disrupting informal social networks. Then we turn to an examination of the compositional dynamics of California prison populations over time to demonstrate that the American justice system has failed to meet their obligations to provide public safety by incapacitating dangerous offenders. We argue that these policy failures represent a breach of the social contract and advocate for more effective collaboration between communities and the formal criminal justice system so that all parties can fulfill their obligations under the contract

An electronic health record based model predicts statin adherence, LDL cholesterol, and cardiovascular disease in the United States Military Health System

<div><p>HMG-CoA reductase inhibitors (or “statins”) are important and commonly used medications to lower cholesterol and prevent cardiovascular disease. Nearly half of patients stop taking statin medications one year after they are prescribed leading to higher cholesterol, increased cardiovascular risk, and costs due to excess hospitalizations. Identifying which patients are at highest risk for not adhering to long-term statin therapy is an important step towards individualizing interventions to improve adherence. Electronic health records (EHR) are an increasingly common source of data that are challenging to analyze but have potential for generating more accurate predictions of disease risk. The aim of this study was to build an EHR based model for statin adherence and link this model to biologic and clinical outcomes in patients receiving statin therapy. We gathered EHR data from the Military Health System which maintains administrative data for active duty, retirees, and dependents of the United States armed forces military that receive health care benefits. Data were gathered from patients prescribed their first statin prescription in 2005 and 2006. Baseline billing, laboratory, and pharmacy claims data were collected from the two years leading up to the first statin prescription and summarized using non-negative matrix factorization. Follow up statin prescription refill data was used to define the adherence outcome (> 80 percent days covered). The subsequent factors to emerge from this model were then used to build cross-validated, predictive models of 1) overall disease risk using coalescent regression and 2) statin adherence (using random forest regression). The predicted statin adherence for each patient was subsequently used to correlate with cholesterol lowering and hospitalizations for cardiovascular disease during the 5 year follow up period using Cox regression. The analytical dataset included 138 731 individuals and 1840 potential baseline predictors that were reduced to 30 independent EHR “factors”. A random forest predictive model taking patient, statin prescription, predicted disease risk, and the EHR factors as potential inputs produced a cross-validated c-statistic of 0.736 for classifying statin non-adherence. The addition of the first refill to the model increased the c-statistic to 0.81. The predicted statin adherence was independently associated with greater cholesterol lowering (correlation = 0.14, p < 1e-20) and lower hospitalization for myocardial infarction, coronary artery disease, and stroke (hazard ratio = 0.84, p = 1.87E-06). Electronic health records data can be used to build a predictive model of statin adherence that also correlates with statins’ cardiovascular benefits.</p></div

Baseline patient and statin characteristics of cohort (N = 138731).

<p>Baseline patient and statin characteristics of cohort (N = 138731).</p

Performance of statin adherence models.

<p>The Receiver operating characteristics (ROC) curves for two models that predict statin adherence defined as percent days covered (PDC) greater than 0.8 during the follow-up period. The results of the risk only model uses random forest modeling and considers baseline demographics, statin prescription characteristics, disease risk predictions, and the ‘factors” resulting from dimension reduction to predict statin adherence. The “risk + first refill” model uses the same predictors as the risk only model but also considers whether or not the first statin prescription was filled and predicts statin adherence for the remaining time period after the first fill. The area represents the area under the ROC curve.</p

Groups of electronic health record codes and their association with higher or lower statin adherence.

<p>Groups of electronic health record codes and their association with higher or lower statin adherence.</p

Predicted statin adherence and risk of cardiovascular outcomes.

<p>Predicted statin adherence was divided into tertiles of predicted statin adherence. The cumulative event free survival for each tertile of risk from Cox survival model is plotted for hospitalizations for acute myocardial infarction, stroke, coronary artery disease, or a composite of all three. P-values represent results of log-rank testing.</p

Independent association between statin adherence model and cardiovascular disease hospitalization.

<p>Independent association between statin adherence model and cardiovascular disease hospitalization.</p