A Machine Learning Method of Determining Causal Inference applied to Shifts in Voting Preferences between 2012-2016

This research investigates the application of machine learning techniques to assist in the execution of a synthetic control model. This model was performed to analyze counties within the United States that showed a voter shift from a majority of Democratic voter share to Republican between the 2012 and 2016 election cycles. The following study applies two steps of machine learning analysis. The first, which is the treatment discovery process, leverages a Random Forest to evaluate feature importance. The second step was the execution of the synthetic control model with two predictor variable lists. The first was the parametric method: a hand curated predictor variable list based on domain knowledge. The second was the non-parametric method: all available predictor (descriptive) variables were used. The Random Forest treatment discovery process resulted in two uncommon variables applied as treatment effects: WIC women enrollment and a decrease of vegetable farm acreage. The opportunity to research these atypical treatment variables allows for the potential of surfacing counterfactual arguments for further research. The use of the parametric and non-parametric methods offers a system of comparison for the research in this paper. The result from the decrease in vegetable farm acreage treatment variable was negative for the non-parametric model. However, the parametric model did show strong statistical evidence towards a treatment effect from the decrease in farm acreage. It is likely that the decrease in vegetable farm acreage is a proxy for poverty or a population density metric. These data results suggest that this model was likely suffering from omitted variable bias for representation of one or both of these metrics in the predictor variable list. The WIC women enrollment treatment variable investigation resulted in the synthetic control model having difficulty in forming a synthetic control comparison. These results suggest there is a fundamental difference between those counties used to create the synthetic control and the other counties that saw a treatment effect. Additional research needs to be performed, and it could result in a different application of the data for use in a synthetic control model. The results of this study, while not surfacing causal inference, did open questions for further research. Given the opportunity these joined causal inference and machines learning practices could continue and potential offer assistance to traditional causal modeling methods. Allowing researchers to understand data and relationships between the data more intimately, theoretically allowing for new causal inferences to be discovered

A Machine Learning Method of Determining Causal Inference applied to Shifts in Voting Preferences between 2012-2016

This research investigates the application of machine learning techniques to assist in the execution of a synthetic control model. This model was performed to analyze counties within the United States that showed a voter shift from a majority of Democratic voter share to Republican between the 2012 and 2016 election cycles. The following study applies two steps of machine learning analysis. The first, which is the treatment discovery process, leverages a Random Forest to evaluate feature importance. The second step was the execution of the synthetic control model with two predictor variable lists. The first was the parametric method: a hand curated predictor variable list based on domain knowledge. The second was the non-parametric method: all available predictor (descriptive) variables were used. The Random Forest treatment discovery process resulted in two uncommon variables applied as treatment effects: WIC women enrollment and a decrease of vegetable farm acreage. The opportunity to research these atypical treatment variables allows for the potential of surfacing counterfactual arguments for further research. The use of the parametric and non-parametric methods offers a system of comparison for the research in this paper. The result from the decrease in vegetable farm acreage treatment variable was negative for the non-parametric model. However, the parametric model did show strong statistical evidence towards a treatment effect from the decrease in farm acreage. It is likely that the decrease in vegetable farm acreage is a proxy for poverty or a population density metric. These data results suggest that this model was likely suffering from omitted variable bias for representation of one or both of these metrics in the predictor variable list. The WIC women enrollment treatment variable investigation resulted in the synthetic control model having difficulty in forming a synthetic control comparison. These results suggest there is a fundamental difference between those counties used to create the synthetic control and the other counties that saw a treatment effect. Additional research needs to be performed, and it could result in a different application of the data for use in a synthetic control model. The results of this study, while not surfacing causal inference, did open questions for further research. Given the opportunity these joined causal inference and machines learning practices could continue and potential offer assistance to traditional causal modeling methods. Allowing researchers to understand data and relationships between the data more intimately, theoretically allowing for new causal inferences to be discovered

A Clinical Program for Transgender and Gender-Diverse Neurodiverse/Autistic Adolescents Developed through Community-Based Participatory Design.

Objective: A series of studies report elevated rates of autism and autistic characteristics among gender-diverse youth seeking gender services. Although youth with the co-occurrence present with complex care needs, existing studies have focused on co-occurrence rates. Further, clinical commentaries have emphasized provider-centered interpretations of clinical needs rather than key stakeholder-driven clinical approaches. This study aimed to employ community-based participatory research methodologies to develop a key stakeholder-driven clinical group program. Method: Autistic/neurodiverse gender-diverse (A/ND-GD) youth (N = 31), parents of A/ND-GD youth (N = 46), A/ND-GD self-advocates (N = 10), and expert clinical providers (N = 10) participated in a multi-stage community-based participatory procedure. Needs assessment data were collected repeatedly over time from A/ND-GD youth and their parents as the youth interacted with one another through ongoing clinical groups, the curriculum of which was developed progressively through the iterative needs assessments. Results: Separate adolescent and parent needs assessments revealed key priorities for youth (e.g., the importance of connecting with other A/ND-GD youth and the benefit of experiencing a range of gender-diverse role models to make gender exploration and/or gender affirmation more concrete) and parents (e.g., the need for A/ND-related supports for their children as well as provision of an A/ND-friendly environment that fosters exploration of a range of gender expressions/options). Integration and translation of youth and parent priorities resulted in 11 novel clinical techniques for this population. Conclusions: With generally high acceptability ratings for each component of the group program, this study presents a community-driven clinical model to support broad care needs and preferences of A/ND-GD adolescents

Comparison of bivalent and monovalent SARS-CoV-2 variant vaccines: the phase 2 randomized open-label COVAIL trial.

Vaccine protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection wanes over time, requiring updated boosters. In a phase 2, open-label, randomized clinical trial with sequentially enrolled stages at 22 US sites, we assessed safety and immunogenicity of a second boost with monovalent or bivalent variant vaccines from mRNA and protein-based platforms targeting wild-type, Beta, Delta and Omicron BA.1 spike antigens. The primary outcome was pseudovirus neutralization titers at 50% inhibitory dilution (ID50 titers) with 95% confidence intervals against different SARS-CoV-2 strains. The secondary outcome assessed safety by solicited local and systemic adverse events (AEs), unsolicited AEs, serious AEs and AEs of special interest. Boosting with prototype/wild-type vaccines produced numerically lower ID50 titers than any variant-containing vaccine against all variants. Conversely, boosting with a variant vaccine excluding prototype was not associated with decreased neutralization against D614G. Omicron BA.1 or Beta monovalent vaccines were nearly equivalent to Omicron BA.1 + prototype or Beta + prototype bivalent vaccines for neutralization of Beta, Omicron BA.1 and Omicron BA.4/5, although they were lower for contemporaneous Omicron subvariants. Safety was similar across arms and stages and comparable to previous reports. Our study shows that updated vaccines targeting Beta or Omicron BA.1 provide broadly crossprotective neutralizing antibody responses against diverse SARS-CoV-2 variants without sacrificing immunity to the ancestral strain. ClinicalTrials.gov registration: NCT05289037

Comparison of bivalent and monovalent SARS-CoV-2 variant vaccines: the phase 2 randomized open-label COVAIL trial.

Acknowledgements: We thank all the participants in this trial; the members of the safety monitoring committee (K. Talaat, J. Treanor, G. Paulsen and D. Stablein), who provided thoughtful discussions resulting in the early trial design; and staff members at Moderna, Pfizer and Sanofi–GSK for their collaboration, scientific input and sharing of documents needed to implement the trial. The COVAIL trial has been funded in part with federal funds from the NIAID and the National Cancer Institute, NIH, under contract HHSN261200800001E 75N910D00024, task order no. 75N91022F00007, and in part by the Office of the Assistant Secretary for Preparedness and Response, Biomedical Advanced Research and Development Authority, under Government Contract no. 75A50122C00008 with Monogram Biosciences, LabCorp. This work was also supported in part with federal funds from the NIAID, NIH, under contract no. 75N93021C00012, and by the Infectious Diseases Clinical Research Consortium (IDCRC) through the NIAID, under award no. UM1AI148684. D.J.S., A.N., S.H.W. and S.T. were supported by the NIH—NIAID Centers of Excellence for Influenza Research and Response (CEIRR) contract no. 75N93021C00014 as part of the SAVE program. D.C.M. and A.E. were supported by the NIAID Collaborative Influenza Vaccine Innovation Centers (CIVICs) contract no. 75N93019C00050. Testing of neutralizing antibody titers by Monogram Biosciences, LabCorp has been funded in part with federal funds from the Department of Health and Human Services, Office of the Assistant Secretary for Preparedness and Response, Biomedical Advanced Research and Development Authority, under contract no. 75A50122C00008. Testing for anti-N-specific antibody was conducted by Cerba Research under contract no. 75N93021D00021. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIH—NIAID.Vaccine protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection wanes over time, requiring updated boosters. In a phase 2, open-label, randomized clinical trial with sequentially enrolled stages at 22 US sites, we assessed safety and immunogenicity of a second boost with monovalent or bivalent variant vaccines from mRNA and protein-based platforms targeting wild-type, Beta, Delta and Omicron BA.1 spike antigens. The primary outcome was pseudovirus neutralization titers at 50% inhibitory dilution (ID50 titers) with 95% confidence intervals against different SARS-CoV-2 strains. The secondary outcome assessed safety by solicited local and systemic adverse events (AEs), unsolicited AEs, serious AEs and AEs of special interest. Boosting with prototype/wild-type vaccines produced numerically lower ID50 titers than any variant-containing vaccine against all variants. Conversely, boosting with a variant vaccine excluding prototype was not associated with decreased neutralization against D614G. Omicron BA.1 or Beta monovalent vaccines were nearly equivalent to Omicron BA.1 + prototype or Beta + prototype bivalent vaccines for neutralization of Beta, Omicron BA.1 and Omicron BA.4/5, although they were lower for contemporaneous Omicron subvariants. Safety was similar across arms and stages and comparable to previous reports. Our study shows that updated vaccines targeting Beta or Omicron BA.1 provide broadly crossprotective neutralizing antibody responses against diverse SARS-CoV-2 variants without sacrificing immunity to the ancestral strain. ClinicalTrials.gov registration: NCT05289037