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This study examines autism diagnosis prevalence within the New York City (NYC) Universal Pre-K for All (UPK) program expansion into racially, ethnically, and socioeconomically diverse NYC neighborhoods. Here, it is hypothesized that racial/ethnic differences in autism diagnoses may close as more children are referred for testing by UPK programs, which they have more thorough interactions with, instead of by public health clinics or other medical avenues. Using NYC Medicaid claim data from 2006 to 2016, descriptive analyses were conducted by estimating linear probability regression and generalized multiple logistic regression to examine whether the probabilities of being diagnosed with autism in comparison to two other disability types (as counterfactuals), learning disabilities (LD) and physical disabilities (PD), differ by race. Subsequently, a difference in difference (DID) strategy (with pre- and post-UPK expansion cohorts) was used to examine the effects of UPK on the probabilities of receiving disability diagnoses. Notably, Latinx and "Other" racially identified children have much higher odds than White children of being diagnosed with autism. By contrast, all non-White groups had much lower odds of being diagnosed with a LD. These findings offer important insight for future UPK and childhood program implementation.

In theory, improving low-income families' housing and neighborhoods could also improve their children's health, through any number of mechanisms. For example, less exposure to environmental toxins could prevent diseases such as asthma; a safer, less violent neighborhood could improve health by reducing the chances of injury and death, and by easing the burden of stress; and a more walkable neighborhood with better playgrounds could encourage children to exercise, making them less likely to become obese. Yet although neighborhood improvement policies generally achieve their immediate goals- investments in playgrounds create playgrounds, for example-Ingrid Gould Ellen and Sherry Glied find that many of these policies don't show a strong effect on poor children's health. One problem is that neighborhood improvements may price low-income families out of the very neighborhoods that have been improved, as new amenities draw more affluent families, causing rents and home prices to rise. Policy makers, say Ellen and Glied, should carefully consider how neighborhood improvements may affect affordability, a calculus that is likely to favor policies with clear and substantial benefits for low-income children, such as those that reduce neighborhood violence. Housing subsidies can help families either cope with rising costs or move to more affluent neighborhoods. Unfortunately, demonstration programs that help families move to better neighborhoods have had only limited effects on children's health, possibly because such transitions can be stressful. And because subsidies go to relatively few low-income families, the presence of subsidies may itself drive up housing costs, placing an extra burden on the majority of families that don't receive them. Ellen and Glied suggest that policy makers consider whether granting smaller subsidies to more families would be a more effective way to use these funds.

We evaluate the impacts of small high schools on youth risky behaviors and mental health in New York City, using a two-sample-instrumental-variable approach to address endogenous school enrollment. We find heterogeneous effects. School size, overall, has little effect. Among students most likely to attend small schools opened after an educational-achievement-oriented reform, however, diagnoses of violence-associated injuries and mental health disorders increased. Among students most likely to attend traditional small schools opened prior to the reform, pregnancy rates and diagnoses of mental health disorders declined. School focus is more important than school size as a determinant of student well-being outcomes. [ABSTRACT FROM AUTHOR]

Parents' health and children's health are closely intertwined--healthier parents have healthier children, and vice versa. Genetics accounts for some of this relationship, but much of it can be traced to environment and behavior, and the environmental and behavioral risk factors for poor health disproportionately affect families living in poverty. Unhealthy children are likely to become unhealthy adults, and poor health drags down both their educational attainment and their income. Because of the close connection between parents' and children's health, write Sherry Glied and Don Oellerich, we have every reason to believe that programs to improve parents' health will improve their children's health as well. Yet few programs aim to work this way, except for a narrow category of programs that target pregnant women, newborns, and very young children. Glied and Oellerich assess these programs, discuss why there are so few of them, and suggest ways to expand them. Their chief conclusion is that structural barriers in the U.S. healthcare system stand in the way of such programs. Some of these barriers have to do with health insurance, access to care, and benefits, but the biggest one is the fact that physicians typically specialize in treating either children or adults, rather than families as a whole. The Affordable Care Act has begun to break down some of these barriers, the authors write, but much remains to be done.

Recent increases in market concentration among health plans, hospitals, and medical groups raise questions about what impact such mergers are having on costs to consumers. We examined the impact of market concentration on the growth of health insurance premiums between 2014 and 2015 in two Affordable Care Act state-based Marketplaces: Covered California and NY State of Health. We measured health plan, hospital, and medical group market concentration using the well-known Herfindahl-Hirschman Index (HHI) and used a multivariate regression model to relate these measures to premium growth. Both states exhibited a positive association between hospital concentration and premium growth and a positive (but not statistically significant) association between medical group concentration and premium growth. Our results for health plan concentration differed between the two states: It was positively associated with premium growth in New York but negatively associated with premium growth in California. The health plan concentration finding in Covered California may be the result of its selectively contracting with health plans.