• 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • Table shows results from models partitioned by sex


    Table 4 shows results from models partitioned by sex. In congo red to the pooled results, Model 2 of Table 4, our fully adjusted model, shows no statistically significant difference in the probability of being a current smoker among the first generation males in the sample by age at migration. This table also shows that relative to the third/higher generation, the probability of being a current smoker is 7.6 percentage points lower for second-generation black males with two foreign-born parents. Among men, there is no statistically significant difference in the probability of being a current smoker among individuals with one-foreign-born and one U.S.-born parent relative to the third/higher generation. However, our findings suggest that among women, there is a more pronounced increase in current smoking across immigrant generations. For example, the marginal effect for immigrant women who arrived at or before age 13 is 4.6 points greater than the marginal effect for women who migrated after age 13 (Table 4, Model 2, Women). In contrast, there is no statistically significant difference between these two groups for men. Similarly, while there is a sizable second-generation advantage in current smoking among second-generation men with two foreign-born parents, this estimate for women is considerably smaller (-0.076 vs. -0.027) and marginally significant. Similar to men, there is no statistically significant difference in the probability of being a current smoker between third/higher generation women and second-generation women with one foreign-born parent. Table 5 shows results for our fully specified model for each of the ancestral subgroups. Similar to the full-sample results (Table 3), first-generation immigrants from each of the ancestral subgroups are substantially less likely to report being current smokers relative to the third/higher generation. Among immigrants from Latin America, the magnitude congo red of this association is stronger among first-generation immigrants who came to the United States after age 13 than for those who migrated at or before age 13. Age at migration does not appear to be associated with the probability of smoking among first-generation West Indian, African, and Haitian immigrants. The second-generation immigrant advantage (relative to the third generation) is largest among individuals with two African-born parents [-0.135 (95% CI: -0.192, -0.078)]. Across each ancestral subgroup, we detect no statistically significant differences in current smoking status between the third/higher generation and second-generation immigrants with only one foreign-born parent. Tables 6 and 7 present these estimates separately for men and women, revealing a similar pattern of smoking as shown in Table 5. Because of the small sample sizes that generate these estimates, however, these results should be viewed with caution.
    Discussion, limitations, and conclusion
    Introduction Mental health disorders are a major worldwide public health concern (Murray & Lopez, 2002), and the societal costs of such disorders are high (Ingoldsby & Shaw, 2002). Mental health and behavior problems often originate in childhood or adolescence (Kessler, Berglund, Demler, Jin, Merikangas & Walters, 2005), and may set youth on a negative trajectory of escalating mental health problems (Ingoldsby & Shaw, 2002). Exposure to disadvantaged neighborhoods is associated with poorer mental health (Leventhal & Brooks-Gunn, 2003), yet suprachiasmic nucleus (SCN) remains unknown what specific mechanisms explain why certain neighborhood characteristics influence health (Macintyre, Ellaway, & Cummins, 2002). We leverage the Moving to Opportunity (MTO) for Fair Housing demonstration, which tested whether receiving a rental voucher to move from disadvantaged neighborhoods improved families’ outcomes, compared to public housing control group families. The MTO study provides strong causal inference and unbiased effects of being offered a housing voucher on outcomes because random assignment ensures that no confounder, measured or unmeasured, is associated with offered treatment, except by chance (Kleinbaum, Sullivan, & Barker, 2007). Moreover, MTO is a policy-relevant treatment, given that over 5 million low-income Americans in over 2 million households use Housing Choice Vouchers, the leading federal affordable housing policy, to subsidize housing costs (Center on Budget & Policy Priorities, 2015). Policy-relevant exposures identify concrete and realistic intervention points that can enhance impacts on health and health disparities (Glymour, Osypuk, & Rehkopf, 2013).