COVID-19 provides a once in a generation opportunity to create a kinder, fairer society. Early signs are not good Pub re-opening prioritised over school re-opening; no significant investment in children’s services or women’s health, a significant determinant of children’s welfare. We highlight the way COVID-19 has, and continues, to harm children and argue that the contemporary erosion of the Birkenhead principle is simply amoral.

Children who have been exposed to public (out-of-home) care experience a range of negative outcomes by late adolescence and early adulthood. The longer-term impact of childhood care is, however, uncertain.

To examine if there is a prospective association between childhood public care and adverse life outcomes in middle-age.

We used data from the UK 1958 birth cohort study of 18558 individuals. PFI-2 nmr Parents reported offspring care status at age 7, 11 and 16. An array of social, criminal, cognitive, and health outcomes was self-reported by cohort members at age 42 (71% response proportion in eligible sample) and a cognitive test battery was administered at age 50 (62% response).

A total of 420 (3.8%) of 11160 people in the analytical sample experienced childhood public care by age 16. Net of confounding factors, experience of public care (vs none) was linked to 11 of the 28 non-mutually exclusive endpoints captured in middle-age, with the most consistent effects apparent for psychosocial characteristics 4/7 sociodemographic (eg, odds ratio; 95% confidence interval for homelessness 2.1; 1.4 to 3.1); 2/2 antisocial (eg, use of illicit drug 2.0; 1.2 to 3.5); 2/3 psychological (eg, mental distress 1.6; 1.2 to 2.1); 1/3 health behaviours (eg, current cigarette smoker 1.7; 1.3 to 2.2); 2/8 somatic health (physical disability 2.7; 1.9 to 3.8); and 0/5 cognitive function (eg, beta coefficient; 95% confidence interval for immediate word recall -0.1; -0.3 to 0.1) endpoints.

The present study suggests that selected associations apparent between childhood care and outcomes in adolescence and early adulthood are also evident in middle-age.

The present study suggests that selected associations apparent between childhood care and outcomes in adolescence and early adulthood are also evident in middle-age.

Most adults do not meet the recommended intake of five portions per day of fruit and vegetables (F&V) in England, but economic analyses of structural policies to change diet are sparse.

Using published data from official statistics and meta-epidemiological studies, we estimated the deaths, years-of-life lost (YLL) and the healthcare costs attributable to consumption of F&V below the recommended five portions per day by English adults. Then, we estimated the cost-effectiveness from governmental and societal perspectives of three policies a universal 10% subsidy on F&V, a targeted 30% subsidy for low-income households and a social marketing campaign (SMC).

Consumption of F&V below the recommended five portions a day accounted for 16321 [10091-23516] deaths and 238767 [170350-311651] YLL in England in 2017, alongside £705951 [398761-1061559] million in healthcare costs. All policies would increase consumption and reduce the disease burden attributable to low intake of F&V. From a societal perspective, the incremental cost-effectiveness ratios were £22891 [22300-25079], £16860 [15589-19763] and £25683 [25237-28671] per life-year saved for the universal subsidy, targeted subsidy and SMC, respectively. At a threshold of £20000 per life-year saved, the likelihood that the universal subsidy, the targeted subsidy and the SMC were cost-effective was 84%, 19% and 5%, respectively. The targeted subsidy would additionally reduce inequalities.

Low intake of F&V represents a heavy health and care burden in England. All dietary policies can improve consumption of F&V, but only a targeted subsidy to low-income households would most likely be cost-effective.

Low intake of F&V represents a heavy health and care burden in England. All dietary policies can improve consumption of F&V, but only a targeted subsidy to low-income households would most likely be cost-effective.Herein, we report abdominal aortic thrombosis as a rare cause of acute spinal cord infarction. A 78-year-old man with multiple vascular risk factors developed acute paraplegia with sensory and urinary disturbances and signs of ischemia in both lower limbs. The post-mortem study done 3 days after the onset of symptoms revealed a large coagulum in the abdominal aorta, distal to the renal arteries and extending to bilateral common iliac arteries; in addition, marked atherosclerosis was present in most large blood vessels. Premature incomplete necrotic foci were seen in the ventral gray matter of the spinal cord from T6 through S5; the surrounding white matter and dorsal gray matter were spared. Considering our autopsy case, spinal cord gray matter may be more vulnerable to ischemia than the white matter.

Nontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research interest; however, unlike ischemic stroke, has not been well studied in large datasets due to the lack of an established administrative claims-based definition. We aimed to evaluate both explicit diagnosis codes and machine learning methods to create a claims-based definition for this clinical phenotype.

We examined all patients admitted to our tertiary medical center with a primary or secondary International Classification of Disease version 9 (ICD-9) or 10 (ICD-10) code for ICH in claims from any portion of the hospitalization in 2014-2015. As a gold standard, we defined the nontraumatic ICH phenotype based on manual chart review. We tested explicit definitions based on ICD-9 and ICD-10 that had been previously published in the literature as well as four machine learning classifiers including support vector machine (SVM), logistic regression with LASSO, random forest and xgboost. We report five standard measures of model lize this definition to address important research gaps.

An explicit ICD-10 definition can be used to accurately identify patients with a nontraumatic ICH phenotype with substantially better performance than ICD-9. An explicit ICD-10 based definition is easier to implement and quantitatively not appreciably improved with the additional application of machine learning classifiers. Future research utilizing large datasets should utilize this definition to address important research gaps.