Objective To develop a method of identifying patients at high risk of readmission to hospital in the next 12 months for practical use by primary care trusts and general practices in the NHS in England. Data sources Data from hospital episode statistics showing all admissions in NHS trusts in England over five years, 1999-2000 to 2003-4; data from the 2001 census for England. Population All residents in England admitted to hospital in the previous four years with a subset of "reference" conditions for which improved management may help to prevent future admissions. Design Multivariate statistical analysis of routinely collected data to develop an algorithm to predict patients at highest risk of readmission in the next 12 months. The algorithm was developed by using a 10% sample of hospital episode statistics data for all of England for the period indicated. The coefficients for 21 most powerful (and statistically significant) variables were then applied against a second 10% test sample to validate the findings of the algorithm from the first sample. Results The key factors predicting subsequent admission included age, sex, ethnicity, number of previous admissions, and clinical condition. The algorithm produces a risk score (from 0 to 100) for each patient admitted with a reference condition. At a risk score threshold of 50, the algorithm identified 54.3% of patients admitted with a reference condition who would have an admission in the next 12 months; 34.7% of patients were "flagged" incorrectly (they would not have a subsequent admission). At risk score threshold levels of 70 and 80, the rate of incorrectly "flagged" patients dropped to 22.6% and 15.7%, but the algorithm found a lower percentage of patients who would be readmitted. The algorithm is made freely available to primary care trusts via a website. Conclusions A method of predicting individual patients at highest risk of readmission to hospital in the next 12 months has been developed, which has a reasonable level of sensitivity and specificity. Using various assumptions a "business case" has been modelled to demonstrate to primary care trusts and practices the potential costs and impact of an intervention using the algorithm to reduce hospital admissions.

This article explores the possible causal pathways through which neighborhoods might affect health and then reviews the existing evidence. Although methodological issues make the literature inconclusive, the authors offer a provisional hypothesis for how neighborhoods shape health outcomes. They hypothesize that neighborhoods may primarily influence health in two ways: first, through relatively short-term influences on behaviors, attitudes, and health-care utilization, thereby affecting health conditions that are most immediately responsive to such influences; and second, through a longer-term process of "weathering," whereby the accumulated stress, lower environmental quality, and limited resources of poorer communities, experienced over many years, erodes the health of residents in ways that make them more vulnerable to mortality from any given disease. Finally, drawing on the more extensive research that has been done exploring the effects of neighborhoods on education and employment, the authors suggest several directions for future research.There is broad consensus that residents of socially and economically deprived communities experience worse health outcomes on average than those living in more prosperous areas. Studies have found that residents of poorer areas suffer from higher rates of heart disease, respiratory ailments, cancer, and overall mortality (

Objective: To assess the impact of a new government-subsidized supermarket in a high-need area on household food availability and dietary habits in children. Design: A difference-in-difference study design was utilized. Setting: Two neighbourhoods in the Bronx, New York City. Outcomes were collected in Morrisania, the target community where the new supermarket was opened, and Highbridge, the comparison community.

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Objective Obesity is a pressing public health problem without proven population-wide solutions. Researchers sought to determine whether a city-mandated policy requiring calorie labeling at fast food restaurants was associated with consumer awareness of labels, calories purchased and fast food restaurant visits. Design and Methods Difference-in-differences design, with data collected from consumers outside fast food restaurants and via a random digit dial telephone survey, before (December 2009) and after (June 2010) labeling in Philadelphia (which implemented mandatory labeling) and Baltimore (matched comparison city). Measures included: self-reported use of calorie information, calories purchased determined via fast food receipts, and self-reported weekly fast-food visits. Results The consumer sample was predominantly Black (71%), and high school educated (62%). Post-labeling, 38% of Philadelphia consumers noticed the calorie labels for a 33 percentage point (p<.001) increase relative to Baltimore. Calories purchased and number of fast food visits did not change in either city over time. Conclusions While some consumer reports noticing and using calorie information, no population level changes were noted in calories purchased or fast food visits. Other controlled studies are needed to examine the longer term impact of labeling as it becomes national law.

Varied diets are diverse with respect to diet quality, and existing dietary variety indices do not capture this heterogeneity. We developed and evaluated the multidimensional US Healthy Food Diversity (HFD) index, which measures dietary variety, dietary quality and proportionality according to the 2010 Dietary Guidelines for Americans (DGA). In the present study, two 24 h dietary recalls from the 2003 -6 National Health and Nutrition Examination Survey (NHANES) were used to estimate the intake of twenty-six food groups and health weights for each food group were informed by the 2010 DGA. The US HFD index can range between 0 (poor) and 1 2 1/n, where n is the number of foods; the score is maximised by consuming a variety of foods in proportions recommended by the 2010 DGA. Energy-adjusted Pearson's correlations were computed between the US HFD index and each food group and the probability of adequacy for fifteen nutrients. Linear regression was run to test whether the index differentiated between subpopulations with differences in dietary quality commonly reported in the literature. The observed mean index score was 0·36, indicating that participants did not consume a variety of healthful foods. The index positively correlated with nutrient-dense foods including whole grains, fruits, orange vegetables and low-fat dairy (r 0·12 to 0·64) and negatively correlated with added sugars and lean meats (r 20·14 to 2 0·23). The index also positively correlated with the mean probability of nutrient adequacy (r 0·41; P,0·0001) and identified non-smokers, women and older adults as subpopulations with better dietary qualities. The US HFD index may be used to inform national dietary guidance and investigate whether healthful dietary variety promotes weight control.

Children who are under care for chronic conditions such as asthma live and manage their illness outside the clinical setting. Their social context matters, and maternal mental health is related to their children's physical health. Although having a child with asthma may be "just" another stressor in the mother's social context, complex treatment plans must be followed despite the many other pressures of neighborhood and family lives.

Objectives. We determined the influence of “water jets” on observed water and milk taking and self-reported fluid consumption in New York City public schools. Methods. From 2010 to 2011, before and 3 months after water jet installation in 9 schools, we observed water and milk taking in cafeterias (mean 1000 students per school) and surveyed students in grades 5, 8, and 11 (n = 2899) in the 9 schools that received water jets and 10 schools that did not. We performed an observation 1 year after implementation (2011–2012) with a subset of schools. We also interviewed cafeteria workers regarding the intervention. Results. Three months after implementation we observed a 3-fold increase in water taking (increase of 21.63 events per 100 students; P < .001) and a much smaller decline in milk taking (-6.73 events per 100 students; P = .012), relative to comparison schools. At 1 year, relative to baseline, there was a similar increase in water taking and no decrease in milk taking. Cafeteria workers reported that the water jets were simple to clean and operate. Conclusions. An environmental intervention in New York City public schools increased water taking and was simple to implement.