Summary Background The Action for Health in Diabetes (Look AHEAD) trial investigated whether long-term cardiovascular disease morbidity and mortality could be reduced through a weight loss intervention among people with type 2 diabetes. Despite finding no significant reduction in cardiovascular events on average, it is possible that some subpopulations might have derived benefit. In this post-hoc analysis, we test the hypothesis that the overall neutral average treatment effect in the trial masked important heterogeneous treatment effects (HTEs) from intensive weight loss interventions. Methods We used causal forest modelling, which identifies HTEs, using a random half of the trial data (the training set). We applied Cox proportional hazards models to test the potential HTEs on the remaining half of the data (the testing set). The analysis was deemed exempt from review by the Columbia University Institutional Review Board, Protocol ID# AAAO3003. Findings Between Aug 22, 2001, and April 30, 2004, 5145 patients with type 2 diabetes were enrolled in the Look AHEAD randomised controlled trial, of whom 4901 were included in the The National Institute of Diabetes and Digestive and Kidney Diseases Repository and included in our analyses: 2450 for model development and 2451 in the testing dataset. Baseline HbA1c and self-reported general health distinguished participants who differentially benefited from the intervention. Cox models for the primary composite cardiovascular outcome revealed a number needed to treat of 28·9 to prevent 1 event over 9·6 years among participants with HbA1c 6·8% or higher, or both HbA1c less than 6·8% and Short Form Health Survey (SF-36) general health score of 48 or more (2101 [86%] of 2451 participants in the testing dataset; 167 [16%] of 1046 primary outcome events for intervention vs 205 [19%] of 1055 for control, absolute risk reduction of 3·46%, 95% CI 0·21–6·73%, p=0·038) By contrast, participants with HbA1c less than 6·8% and baseline SF-36 general health score of less than 48 (350 [14%] of 2451 participants in the testing data; 27 [16%] of 171 primary outcome events for intervention vs 15 [8%] of 179 primary outcome events for control) had an absolute risk increase of the primary outcome of 7·41% (0·60 to 14·22, p=0·003). Interpretation Look AHEAD participants with moderately or poorly controlled diabetes (HbA1c 6·8% or higher) and subjects with well controlled diabetes (HbA1c less than 6·8%) and good self-reported health (85% of the overall study population) averted cardiovascular events from a behavioural intervention aimed at weight loss. However, 15% of participants with well controlled diabetes and poor self-reported general health experienced negative effects that rendered the overall study outcome neutral. HbA1c and a short questionnaire on general health might identify people with type 2 diabetes likely to derive benefit from an intensive lifestyle intervention aimed at weight loss. Funding None.

Objectives. To examine the relationship between aggressive enforcement of anti-immigration policies and mental health among Hispanics/Latinos in the United States before and after major national immigration policy changes. Methods. Data were drawn from Behavioral Risk Factor Surveillance System surveys administered from 2014 to 2018. The exposure was the rate of immigration arrests in the 2 months before the survey date within the respondent’s state of residence. Outcomes included past-month reporting of (1) number of days of poor mental health, (2) at least 1 day of poor mental health, and (3) frequent mental distress. Results. There was no relationship between arrest rates and mental health among Hispanic/Latino respondents across the overall period. After consideration of policy changes, however, a 1-percentage-point increase in a state’s immigration arrest rate in the postpolicy period was significantly associated with each mental health morbidity outcome. Conclusions. We found evidence supporting an association between worsening mental health among Hispanics/Latinos and increased arrest rates following the announcement of several restrictive immigration policies. The potential public health effects of aggressive immigration enforcement must be better acknowledged and addressed in immigration debates.

A BS TRACT: Background: Despite extensive research regarding the etiology of Huntington's disease, relatively little is known about the epidemiology of this rare disorder, particularly in the United States where there are no national-scale estimates of the disease. Objectives: To provide national-scale estimates of Huntington's disease in a U.S. population and to test whether disease rates are increasing, and whether frequency varies by race, ethnicity, or other factors. Methods: Using an insurance database of over 67 million enrollees, we retrospectively identified a cohort of 3,707 individuals diagnosed with Huntington's disease between 2003 and 2016. We estimated annual incidence, annual diagnostic frequency, and tested for trends over time and differences in diagnostic frequency by sociodemographic characteristics. Results: During the observation period, the age-adjusted cumulative incidence rate was1.22 per 100,000 persons (95% confidence interval: 1.53, 1.65), and age-adjusted diagnostic frequency was 6.52 per 100,000 persons (95% confidence interval: 5.31, 5.66); both rates remained relatively stable over the 14-year period. We identified several previously unreported differences in Huntington's disease frequency by selfreported sex, income, and race/ethnicity. However, racial/ ethnic differences were of lower magnitude than have previously been reported in other country-level studies. Conclusions: In these large-scale estimates of U.S. Huntington's disease epidemiology, we found stable disease frequency rates that varied by several sociodemographic factors. These findings suggest that disease patterns may be more driven by social or environmental factors than has previously been appreciated. Results further demonstrate the potential utility of administrative Big Data in rare disease epidemiology when other data sources are unavailable.