Background: Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposure factors into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility. Methods: We presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003-2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models. Results: PLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects. Conclusions: We presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.

Background: The social determinants of health (SDOH) are the conditions in which people are born, grow, work, live, and age. Lack of SDOH training of dental providers on SDOH may result in suboptimal care provided to pediatric dental patients and their families. The purpose of this pilot study is to assess the feasibility and acceptability of SDOH screening and referral by pediatric dentistry residents and faculty in the dental clinics of Family Health Centers at NYU Langone (FHC), a Federally Qualified Health Center (FQHC) network in Brooklyn, NY.Methods: Guided by the Implementation Outcomes Framework, 15 pediatric dentists and 40 pediatric dental patient–parent/guardian dyads who visited FHC in 2020-2021 for recall or treatment appointments participated in this study. The a priori feasibility and acceptability criteria for these outcomes were that after completing the Parent Adversity Scale (a validated SDOH screening tool), >80% of the participating parents/guardians would feel comfortable completing SDOH screening and referral at the dental clinic (acceptable) and >80% of the participating parents/guardians who endorsed SDOH needs would be successfully referred to an assigned counselor at the Family Support Center (feasible). The key covariate was age of the pediatric patient.Results: The mean age of the pediatric dental patients was 7.3 y (SD=3.0). Most of the participating parents/guardians were the mothers of the pediatric dental patients (87.5%) and self-reported as being Hispanic (65.0%). Post-intervention, 83.9% of the participating parents/guardians who expressed an SDOH need were successfully referred to an assigned counselor at the Family Support Center for follow-up and 95.0% of the participating parents/guardians felt comfortable completing the questionnaire at the dental clinic, surpassing the a priori feasibility and acceptability criteria, respectively. Further, while most (80.0%) of the participating dental providers reported being trained in SDOH, only one-third (33.3%) usually or always assess SDOH for their pediatric dental patients and most (53.8%) felt minimally comfortable discussing challenges faced by pediatric dental patient families and referring patients to resources in the community.Conclusions: This study provides novel evidence of the feasibility and acceptability of SDOH screening and referral by dentists in the pediatric dental clinics of an FQHC network.

Background: Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposures into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility. Methods: We presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003-2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models. Results: PLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects. Conclusions: We presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.