A Varying Coefficient Model to Jointly Test Genetic and Gene-Environment Interaction Effects.

Most human traits are influenced by the interplay between genetic and environmental factors. Many statistical methods have been proposed to screen for gene-environment interaction (GxE) in the post genome-wide association study era. However, most of the existing methods assume a linear interaction between genetic and environmental factors toward phenotypic variations, which diminishes statistical power in the case of nonlinear GxE. In this paper, we present a flexible statistical procedure to detect GxE regardless of whether the underlying relationship is linear or not. By modeling the joint genetic and GxE effects as a varying-coefficient function of the environmental factor, the proposed model is able to capture dynamic trajectories of GxE. We employ a likelihood ratio test with a fast Monte Carlo algorithm for hypothesis testing. Simulations were conducted to evaluate validity and power of the proposed model in various settings. Real data analysis was performed to illustrate its power, in particular, in the case of nonlinear GxE.

Interactive association of chronic illness and food insecurity with emergency department utilization among school-age children in the United States: A cross-sectional study.

Background and Aims: Food insecurity combined with chronic disease conditions is a risk factor for Emergency Department (ED) utilization, an indicator of poor quality of care. However, such an association is not certain among school-age children with chronic conditions. Therefore, we aim to determine the association of food insecurity, chronic conditions, and ED utilization among school-age children in the United States. Methods: We analyzed the data from the 2017 Medical expenditure panel survey (MEPS) among children aged 6-17 years (N = 5518). MEPS data was released electronically by the Agency for Healthcare Research and Quality (AHRQ). We identified four groups of school-age children based on the presence of food security and chronic conditions: 1) with food insecurity and chronic conditions; 2) no food insecurity and chronic conditions; 3) with food insecurity and no chronic conditions; and 4) no food insecurity and no chronic conditions. We compared ED utilization among these four groups using incidence rate ratios (IRR) after adjusting children's age, sex, race and ethnicity, household income, insurance coverage, obesity, and geographic region using count data model, specifically multivariable Poison regression. We used SAS 9.4 and STATA 14.2 for all the data analyses. Results: There were unweighted 5518 school-age children who represented weighted 50,479,419 school-age children in the final analysis. Overall, 6.0% had food insecurity with chronic conditions. These children had higher ED utilization (19.7%) than the other three groups (13.3%, 8.8%, and 7.2%, p < 0.001). The adjusted IRR of ED utilization among school-age children with food insecurity and chronic conditions was 1.90 (95% confidence interval 1.20-3.01, p = 0.007) compared with those with food security and chronic conditions. Conclusion: One in 16 school-age children has both food insecurity and chronic conditions. Food insecurity was positively associated with frequent ED visits in the presence of chronic conditions. Therefore, addressing food insecurity may reduce the risk of ED visits.

Factors Associated with Reducing Disparities in Electronic Personal Heath Records Use Among Non-Hispanic White and Hispanic Adults.

OBJECTIVES: Personal health records (PHR) use has improved individuals' health outcomes. The adoption of PHR remains low with documented racial disparities. We aim to determine factors associated with reducing racial and ethnic disparities among Hispanic adults in PHR use. METHODS: Participants included non-Hispanic White (NHW) and Hispanic adults (age ≥ 18 years) enrolled in Health Information National Trends Survey in 2018 and 2019. We identified PHR use as online medical record access in the last 12 months. We considered three factors (1. accessing mHealth Apps on the phone, 2. having a usual source of care, and 3. electronically communicating (e-communication) with healthcare providers) as facilitating PHR use. Multivariable logistic regressions with replicate weights were analyzed to determine factors associated with racial/ethnic disparities in PHR use after controlling for general characteristics (i.e., sex, age, education, insurance status, and income). RESULTS: A lower percentage of Hispanics than NHWs used PHR (42.0% vs. 53.5%, P < .001). When adjusted for individual general characteristics, the adjusted odds ratio (AOR) of e-communication with healthcare providers associated with PHR use was 1.49 (1.19-1.86, P < .001), AOR was 2.06 (1.62-2.6, P < .001) on accessing to mHealth App, and 2.60 (1.86-3.63, P < .001) on having a usual source of care. However, the racial difference was not statistically significant after adjusting three factors promoting PHR use (AOR = 0.90, 95% CI = 0.66, 1.22, P = .48). CONCLUSIONS: Ethnic disparities were reduced when PHR use was facilitated by having a usual source of care, active e-communication, and having access to mHealth apps. Interventions focusing on these three factors may potentially reduce racial/ethnic disparities.

A nonparametric alternative to the Cochran-Armitage trend test in genetic case-control association studies: The Jonckheere-Terpstra trend test.

Identifications of novel genetic signals conferring susceptibility to human complex diseases is pivotal to the disease diagnosis, prevention, and treatment. Genetic association study is a powerful tool to discover candidate genetic signals that contribute to diseases, through statistical tests for correlation between the disease status and genetic variations in study samples. In such studies with a case-control design, a standard practice is to perform the Cochran-Armitage (CA) trend test under an additive genetic model, which suffers from power loss when the model assumption is wrong. The Jonckheere-Terpstra (JT) trend test is an alternative method to evaluate association in a nonparametric way. This study compares the power of the JT trend test and the CA trend test in various scenarios, including different sample sizes (200-2000), minor allele frequencies (0.05-0.4), and underlying modes of inheritance (dominant genetic model to recessive genetic model). By simulation and real data analysis, it is shown that in general the JT trend test has higher, similar, and lower power than the CA trend test when the underlying mode of inheritance is dominant, additive, and recessive, respectively; when the sample size is small and the minor allele frequency is low, the JT trend test outperforms the CA trend test across the spectrum of genetic models. In sum, the JT trend test is a valuable alternative to the CA trend test under certain circumstances with higher statistical power, which could lead to better detection of genetic signals to human diseases and finer dissection of their genetic architecture.

The Association of Multimorbidity With Whole Health Activities Among Adults in the United States: Evidence From the NHIS and BRFSS.

Background: Whole health is a holistic approach encompassing integrative medicine, emotional, and spiritual health and is critical to improving health outcomes among individuals with multimorbidity. Objective: To examine the prevalence of Whole Health activities and the association of multimorbidity and Whole Health activities using nationally representative datasets. Methods: As no single dataset has information on Whole Health self-care activities, data from the 2017 National Health Interview Survey (n = 25 134) was used to measure participants' mind-body therapy usage, sleep, mental health, and physical activity. We used the 2017 Behavioral Risk Factor Surveillance System (n = 347 029) to assess regular vegetable and/or fruit consumption. Results: A significantly lower percentage of adults with multimorbidity had adequate sleep (58.2%vs.67.1%), no psychological distress (71.8%vs.82.1%), adequate physical activity (48.2%vs.62.1%), and regular vegetable and/or fruit consumption (54.2%vs.56.6%) compared to those without multimorbidity. Although lower percentages of adults with multimorbidity utilized mind-body therapies (22.9%vs.25.2%), the association was reversed when adjusted for socioeconomic factors. In the fully adjusted models, adults with multimorbidity were more likely to use mind-body therapies (AOR = 1.19, 95%CI = 1.09, 1.31). Furthermore, when adjusting for other independent variables, the associations of multimorbidity with sleep, psychological distress, and diet were exacerbated, and the association of multimorbidity with physical activity was attenuated. Conclusion: Adults with multimorbidity were less likely to engage in most of the Whole Health activities except mind-body therapies compared to the no multimorbidity group. Findings suggest that adjustment for other factors such as age and socioeconomic status changed the magnitude and direction of the association of multimorbidity with Whole Health activities.

Association of multimorbidity with the use of health information technology.

Objective: To examine the association of multimorbidity with health information technology use among adults in the USA. Methods: We used cross-sectional study design and data from the Health Information National Trends Survey 5 Cycle 4. Health information technology use was measured with ten variables comprising access, recent use, and healthcare management. Unadjusted and adjusted logistic and multinomial logistic regressions were used to model the associations of multimorbidity with health information technology use. Results: Among adults with multimorbidity, health information technology use for specific purposes ranged from 37.8% for helping make medical decisions to 51.7% for communicating with healthcare providers. In multivariable regressions, individuals with multimorbidity were more likely to report general use of health information technology (adjusted odds ratios = 1.48, 95% confidence intervals = 1.01-2.15) and more likely to use health information technology to check test results (adjusted odds ratios = 1.85, 95% confidence intervals = 1.33-2.58) compared to adults with only one chronic condition, however, there were no significant differences in other forms of health information technology use. We also observed interactive associations of multimorbidity and age on various components of health information technology use. Compared to younger adults with multimorbidity, older adults (≥ 65 years of age) with multimorbidity were less likely to use almost all aspects of health information technology. Conclusion: Health information technology use disparities by age and multimorbidity were observed. Education and interventions are needed to promote health information technology use among older adults in general and specifically among older adults with multimorbidity.