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We introduce a novel meta-analysis framework to combine dependent tests under a general setting, and utilize it to synthesize various microbiome association tests that are calculated from the same dataset. Our development builds upon the classical meta-analysis methods of aggregating P-values and also a more recent general method of combining confidence distributions, but makes generalizations to handle dependent tests. The proposed framework ensures rigorous statistical guarantees, and we provide a comprehensive study and compare it with various existing dependent combination methods. Notably, we demonstrate that the widely used Cauchy combination method for dependent tests, referred to as the vanilla Cauchy combination in this article, can be viewed as a special case within our framework. Moreover, the proposed framework provides a way to address the problem when the distributional assumptions underlying the vanilla Cauchy combination are violated. Our numerical results demonstrate that ignoring the dependence among the to-be-combined components may lead to a severe size distortion phenomenon. Compared to the existing P-value combination methods, including the vanilla Cauchy combination method and other methods, the proposed combination framework is flexible and can be adapted to handle the dependence accurately and utilizes the information efficiently to construct tests with accurate size and enhanced power. The development is applied to the microbiome association studies, where we aggregate information from multiple existing tests using the same dataset. The combined tests harness the strengths of each individual test across a wide range of alternative spaces, enabling more efficient and meaningful discoveries of vital microbiome associations.

 

Using restricted data from 2011 to 2014, this study examines whether neighborhood immigrant concentration and survey interview language are associated with participation in the National Crime Victimization Survey (NCVS). The findings show that survey participation in the NCVS during the study period did not differ appreciably among households and persons sampled from neighborhoods with larger shares of immigrants. This suggests that the NCVS can contribute meaningfully to knowledge about the relationship between neighborhood immigrant concentration and levels of crime, providing an important complement to studies based on crime data collected by law enforcement agencies. Interview language had a minimal impact on nonresponse among Hispanic respondents in the NCVS, but the study revealed much higher rates of nonresponse across waves among Asian household respondents who completed the NCVS in a non-English language, especially among those from neighborhoods with relatively low immigrant concentration. This suggests that greater translation support for Asian respondents could increase NCVS response rates. Replicating and extending our research with more recent NCVS data, and incorporating the new item on citizenship status, would be valuable given the continued growth in the immigrant population, increased share of immigrants who routinely speak a language other than English at home, and social and political changes that have corresponded with observed reductions in nonresponse in government-administered surveys. We encourage the Bureau of Justice Statistics (BJS) to facilitate such research by routinely making the restricted NCVS data available for researchers to use within the nation’s Federal Statistical Research Data Centers and by adding interview language as a permanent fixture of the data.

 

Our understanding of how immigration enforcement impacts crime has been informed exclusively by data from police crime statistics. This study complements existing research by using longitudinal multilevel data from the National Crime Victimization Survey for 2005–2014 to simultaneously assess the impact of the three predominant immigration policies that have been implemented in local communities. The results indicate that the activation of Secure Communities and 287(g) task force agreements significantly increased violent victimization risk among Latinos, whereas they showed no evident impact on victimization risk among non‐Latino Whites and Blacks. The activation of 287(g) jail enforcement agreements and anti‐detainer policies had no significant impact on violent victimization risk during the period.

Contrary to their stated purpose of enhancing public safety, our results show that the Secure Communities program and 287(g) task force agreements did not reduce crime, but instead eroded security in U.S. communities by increasing the likelihood that Latinos experienced violent victimization. These results support the Federal government's ending of 287(g) task force agreements and its more recent move to end the Secure Communities program. Additionally, the results of our study add to the evidence challenging claims that anti‐detainer policies pose a threat to violence risk.

 

Until recently, national-level data on criminal victimization in the United States did not include information on immigrant or citizenship status of respondents. This data-infrastructure limitation has hindered scientific understanding of whether immigrants are more or less likely than native-born Americans to be criminally victimized and how victimization may vary among immigrants of different statuses. We address these issues in the present study by using new data from the 2017–2018 National Crime Victimization Survey (NCVS) to explore the association between citizenship status and victimization risk in a nationally representative sample of households and persons aged 12 years and older. The research is guided by a theoretical framing that integrates insights from studies of citizenship with the literature on immigration and crime, as well as with theories of victimization. We find that a person’s foreign-born status (but not their acquired U.S. citizenship) confers protection against victimization. We also find that the protective benefit associated with being foreign born does not extend to those with ambiguous citizenship status, who in our data exhibit attributes similar to the known characteristics of undocumented immigrants. We conclude by discussing the implications of our findings and the potential ways to extend the research.