RESUMEN
The purpose of this study was to characterize the patient and provider engagement in the sudden telehealth implementation that occurred with the onset of the COVID-19 pandemic. Patients and providers from 3 nurse-led models of care (federally qualified health centers, nurse midwifery practices, and the Nurse-Family partnership program) in Colorado were surveyed. Data from the Patient Attitude toward Telehealth survey and Provider Perceptions about Telehealth were collected. Patient respondents (n = 308) who resided primarily in rural or frontier communities were female, white, and Hispanic. Patients in urban areas used telehealth more frequently than in rural or frontier areas (P < .001). Rural/Frontier patients had significantly lower attitude scores than urban patients across each of 5 domains assessed. Telehealth modality differed across location (P < .023), with video calls, used more frequently by urban providers, and phone calls used by rural/frontier providers. Our data highlight differences in telehealth access and attitudes across rurality. These findings may contribute to future policy while addressing barriers to telehealth access and delivery.
RESUMEN
BACKGROUND: To identify genetic associations of quantitative metabolic syndrome (MetS) traits and characterize heterogeneity across ethnic groups. METHODS: Data was collected from GENetics of Noninsulin dependent Diabetes Mellitus (GENNID), a multiethnic resource of Type 2 diabetic families and included 1520 subjects in 259 African-American, European-American, Japanese-Americans, and Mexican-American families. We focused on eight MetS traits: weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein, triglycerides, fasting glucose, and insulin. Using genotyped and imputed data from Illumina's Multiethnic array, we conducted genome-wide association analyses with linear mixed models for all ethnicities, except for the smaller Japanese-American group, where we used additive genetic models with gene-dropping. RESULTS: Findings included ethnic-specific genetic associations and heterogeneity across ethnicities. Most significant associations were outside our candidate linkage regions and were coincident within a gene or intergenic region, with two exceptions in European-American families: (a) within previously identified linkage region on chromosome 2, two significant GLI2-TFCP2L1 associations with weight, and (b) one chromosome 11 variant near CADM1-LINC00900 with pleiotropic blood pressure effects. CONCLUSIONS: This multiethnic family study found genetic heterogeneity and coincident associations (with one case of pleiotropy), highlighting the importance of including diverse populations in genetic research and illustrating the complex genetic architecture underlying MetS.
RESUMEN
Genome-wide association studies (GWAS) have been used to establish thousands of genetic associations across numerous phenotypes. To improve the power of GWAS and generalize associations across ethnic groups, transethnic meta-analysis methods are used to combine the results of several GWAS from diverse ancestries. The goal of this study is to identify genetic associations for eight quantitative metabolic syndrome (MetS) traits through a meta-analysis across four ethnic groups. Traits were measured in the GENetics of Noninsulin dependent Diabetes Mellitus (GENNID) Study which consists of African-American (families = 73, individuals = 288), European-American (families = 79, individuals = 519), Japanese-American (families = 17, individuals = 132), and Mexican-American (families = 113, individuals = 610) samples. Genome-wide association results from these four ethnic groups were combined using four meta-analysis methods: fixed effects, random effects, TransMeta, and MR-MEGA. We provide an empirical comparison of the four meta-analysis methods from the GENNID results, discuss which types of loci (characterized by allelic heterogeneity) appear to be better detected by each of the four meta-analysis methods in the GENNID Study, and validate our results using previous genetic discoveries. We specifically compare the two transethnic methods, TransMeta and MR-MEGA, and discuss how each transethnic method's framework relates to the types of loci best detected by each method.
