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Empirical evidence points to a persistent Black-White racial gap in police-caused homicides. Some scholarship treats the gap as denoting criminal justice exposure either in terms of involvement in crime or living in a high-crime context. By contrast, health scholarship typically points to the importance of racism including the attitudes, institutional practices, and overall structures that operate to privilege one group over another. Still, given the demographics of US society, the Black-White racial contrast overlooks the 25% of Americans who are neither Black nor White: Native Americans, Latinos, and Asians. The question of how the groups should be organized vis-a-vis the current Black-White model and theories arises. An answer is not straightforward. There is a rank-ordering to the groups' mortality rates as well as an exponential increase in the number of possible comparisons. In this paper we systematically review the literature on race and police-caused homicide with a particular focus on studies that attempt to move beyond the Black-White model. We find that studies on race and police-caused homicide either make no comparison between the groups, or, alternatively, use a White-non-White, a Black-non-Black, and/or a Black-Native American-Latino vs. White-Asian comparison. We use data on group-specific mortality rates to examine the strengths and limits of each of these practices. The limits are the selection of counterfactual gaps, the selection of smaller gaps, and/or the omission of larger gaps. To address these limits, we propose that a Black-Native American vs. Latino-White-Asian model best captures the higher and lower mortality rates in police-caused homicide data. • Mortality rates from police-caused homicide vary by race and ethnicity. • The Black group has the highest rate followed by the Native American, Latino, White and Asian groups. • The literature makes one of several contrasts between these groups. • These are White-non-White; Black-non-Black, or Black-Native American-Latino vs. White-Asian. • Black-Native American vs. Latino-White-Asian contrast best reflects the groups' mortality rates. [ABSTRACT FROM AUTHOR]

Socioeconomic inequalities in childhood Body Mass Index (BMI) are becoming increasingly more pronounced across the world. Although countries differ in the direction and strength of these inequalities, cross-national comparative research on this topic is rare. This paper draws on harmonized longitudinal cohort data from four wealthy countries—Germany, the Netherlands, the United Kingdom (UK), and the United States (US)—to 1) map cross-country differences in the magnitude of socioeconomic inequalities in childhood BMI, and 2) to examine cross-country differences in the role of three energy-balance-related behaviors—physical activity, screen time, and breakfast consumption—in explaining these inequalities. Children were aged 5–7 at our first timepoint and were followed up at age 8–11. We used data from the German National Educational Panel Study, the Dutch Generation R study, the UK Millennium Cohort Study and the US Early Childhood Longitudinal-Kindergarten Study. All countries revealed significant inequalities in childhood BMI. The US stood out in having the largest inequalities. Overall, inequalities between children with low versus medium educated parents were smaller than those between children with high versus medium educated parents. The role of energy-balance-related behaviors in explaining inequalities in BMI was surprisingly consistent. Across countries, physical activity did not, while screen time and breakfast consumption did play a role. The only exception was that breakfast consumption did not play a role in the US. Cross-country differences emerged in the relative contribution of each behavior in explaining inequalities in BMI: Breakfast consumption was most important in the UK, screen time explained most in Germany and the US, and breakfast consumption and screen time were equally important in the Netherlands. Our findings suggest that what constitutes the most effective policy intervention differs across countries and that these should target both children from medium as well as low educated families. • All countries showed substantial socioeconomic inequalities in childhood BMI. • In all countries, inequalities in BMI steepen over course of primary school. • Protective factors among the most advantaged seem to mainly drive inequalities. • Across most countries, underlying mechanisms for inequalities in BMI are similar. • The relative contribution of each mechanism to BMI inequalities differs per country. [ABSTRACT FROM AUTHOR]

Although local policies aimed at reducing childhood health inequities can benefit from local data, sample size constraints in population representative health surveys often prevent rigorous evaluations of child health disparities and health care patterns at local levels. Electronic Health Records (EHRs) offer a possible solution as they contain large amounts of information on pediatric patients within a health system. In this paper, we consider the suitability of using EHRs from a large health system to study local children's health by evaluating the extent to which the EHRs capture the county's child population. First, we compare the demographic characteristics of Duke University Health System pediatric patients who live in Durham County, NC (USA) to the child population estimates in the 2015–2019 American Community Survey. We then examine geographic variation in census tract rates of children captured in the EHR data and estimate negative binomial models to assess how tract characteristics are associated with these rates. We also perform these analyses for the subset of pediatric patients who have a well-child encounter. We find that the demographic characteristics of pediatric patients captured by the EHRs are similar to those of the county's child population. Although the county rate of children captured in the EHRs is high, there is variation across census tracts. On average, census tracts with higher concentrations of non-Hispanic Black residents have lower capture rates and tracts with higher concentrations of poverty have higher capture rates, with the poorest tracts showing the largest racial gap in rates of children captured by EHRs. Our findings suggest that EHRs from a large health system can be used to assess children's population health, but that EHR-based evaluations of children's health disparities and health care patterns should account for differences in who is captured by the EHRs based on census tract characteristics. • EHRs from a large health system are suitable for studying local children's health. • Local children in EHRs are demographically similar to county population estimates. • Rates of children captured in health system differ by census tract characteristics. • EHR-based analyses should adjust for local differences in capture rates. [ABSTRACT FROM AUTHOR]