Dataset Information

INFERENCE FOR SOCIAL NETWORK MODELS FROM EGOCENTRICALLY SAMPLED DATA, WITH APPLICATION TO UNDERSTANDING PERSISTENT RACIAL DISPARITIES IN HIV PREVALENCE IN THE US.

ABSTRACT: Egocentric network sampling observes the network of interest from the point of view of a set of sampled actors, who provide information about themselves and anonymized information on their network neighbors. In survey research, this is often the most practical, and sometimes the only, way to observe certain classes of networks, with the sexual networks that underlie HIV transmission being the archetypal case. Although methods exist for recovering some descriptive network features, there is no rigorous and practical statistical foundation for estimation and inference for network models from such data. We identify a subclass of exponential-family random graph models (ERGMs) amenable to being estimated from egocentrically sampled network data, and apply pseudo-maximum-likelihood estimation to do so and to rigorously quantify the uncertainty of the estimates. For ERGMs parametrized to be invariant to network size, we describe a computationally tractable approach to this problem. We use this methodology to help understand persistent racial disparities in HIV prevalence in the US. We also discuss some extensions, including how our framework may be applied to triadic effects when data about ties among the respondent's neighbors are also collected.

SUBMITTER: Krivitsky PN

PROVIDER: S-EPMC5737754 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

INFERENCE FOR SOCIAL NETWORK MODELS FROM EGOCENTRICALLY SAMPLED DATA, WITH APPLICATION TO UNDERSTANDING PERSISTENT RACIAL DISPARITIES IN HIV PREVALENCE IN THE US.

Krivitsky Pavel N PN Morris Martina M

The annals of applied statistics 20170301 1

Egocentric network sampling observes the network of interest from the point of view of a set of sampled actors, who provide information about themselves and anonymized information on their network neighbors. In survey research, this is often the most practical, and sometimes the only, way to observe certain classes of networks, with the sexual networks that underlie HIV transmission being the archetypal case. Although methods exist for recovering some descriptive network features, there is no ri ...[more]

PMID: 29276550

Similar Datasets

Project description:ImportanceChildhood asthma is characterized by disparities in the experience of morbidity, including the risk for readmission to the hospital after an initial hospitalization. African American children have been shown to have more than 2 times the hazard of readmission when compared with their white counterparts.ObjectiveTo explain why African American children are at greater risk for asthma-related readmissions than white children.Design, setting, and participantsThis study was completed as part of the Greater Cincinnati Asthma Risks Study, a population-based, prospective, observational cohort. From August 2010 to October 2011, it enrolled 695 children, aged 1 to 16 years, admitted for asthma or wheezing who identified as African American (n = 441) or white (n = 254) in an inpatient setting of an urban, tertiary care children's hospital.Main outcomes and measuresThe main outcome was time to asthma-related readmission and race was the predictor. Biologic, environmental, disease management, access, and socioeconomic hardship variables were measured; their roles in understanding racial readmission disparities were conceptualized using a directed acyclic graphic. Inverse probability of treatment weighting balanced African American and white children with respect to key measured variables. Racial differences in readmission hazard were assessed using weighted Cox proportional hazards regression and Kaplan-Meier curves.ResultsThe sample was 65% male (n = 450), and the median age was 5.4 years. African American children were 2.26 times more likely to be readmitted than white children (95% CI, 1.56-3.26). African American children significantly differed with respect to nearly every measured biologic, environmental, disease management, access, and socioeconomic hardship variable. Socioeconomic hardship variables explained 53% of the observed disparity (hazard ratio, 1.47; 95% CI, 1.05-2.05). The addition of biologic, environmental, disease management, and access variables resulted in 80% of the readmission disparity being explained. The difference between African American and white children with respect to readmission hazard no longer reached the level of significance (hazard ratio, 1.18; 95% CI, 0.87-1.60; Cox P = .30 and log-rank P = .39).Conclusions and relevanceA total of 80% of the observed readmission disparity between African American and white children could be explained after statistically balancing available biologic, environmental, disease management, access to care, and socioeconomic and hardship variables across racial groups. Such a comprehensive, well-framed approach to exposures that are associated with morbidity is critical as we attempt to better understand and lessen persistent child asthma disparities.

Project description:ImportanceEmerging data suggest gout and hyperuricemia may now be more frequent among Black adults in the US than White adults, especially Black women. However, national-level, sex-specific general population data on racial differences in gout prevalence and potential socioclinical risk factors are lacking.ObjectiveTo identify sex-specific factors driving disparities between Black and White adults in contemporary gout prevalence in the US general population.Design, setting, and participantsThis cross-sectional analysis used nationally representative, decadal survey data from successive cycles of the National Health and Nutrition Examination Survey from 2007 to 2016. Data were analyzed from November 1, 2019, through May 31, 2021. Participants included US adults self-reporting Black or White race.ExposuresSelf-reported race, excess body mass index, chronic kidney disease (CKD; defined as estimated glomerular filtration rate <60 mL/min/1.73 m2, according to latest equations without race coefficient), poverty, poor-quality diet, low educational level, alcohol consumption, and diuretic use.Main outcomes and measuresRace- and sex-specific prevalence of physician- or clinician-diagnosed gout and hyperuricemia and their differences before and after adjusting for potential socioclinical risk factors.ResultsA total of 18 693 participants were included in the analysis, consisting of 3304 Black women (mean [SD] age, 44.8 [0.4] years), 6195 White women (mean [SD] age, 49.8 [0.3] years), 3085 Black men (mean [SD] age, 43.6 [0.5] years]), and 6109 White men (mean [SD] age, 48.2 [0.3] years). Age-standardized prevalence of gout was 3.5% (95% CI, 2.7%-4.3%) in Black women and 2.0% (95% CI, 1.5%-2.5%) in White women (age-adjusted odds ratio [OR], 1.81 [95% CI, 1.29-2.53]); prevalence was 7.0% (95% CI, 6.2%-7.9%) in Black men and 5.4% (95% CI, 4.7%-6.2%) in White men (age-adjusted OR, 1.26 [95% CI, 1.02-1.55]). These associations attenuated after adjusting for poverty, diet, body mass index, and CKD among women and for diet and CKD among men but became null after adjusting for all risk factors (ORs, 1.05 [95% CI, 0.67-1.65] among women and 1.05 [95% CI, 0.80-1.35] among men). Hyperuricemia end point findings were similar.Conclusions and relevanceIn this nationally representative race- and sex-specific cross-sectional study of US adults, gout was more prevalent in adults self-reporting Black race during a recent 10-year period compared with their White counterparts. These racial differences may be explained by sex-specific differences in diet and social determinants of health and clinical factors. Culturally informed efforts focusing on these factors could reduce current gout-related disparities.

Dataset Information

INFERENCE FOR SOCIAL NETWORK MODELS FROM EGOCENTRICALLY SAMPLED DATA, WITH APPLICATION TO UNDERSTANDING PERSISTENT RACIAL DISPARITIES IN HIV PREVALENCE IN THE US.

Publications

INFERENCE FOR SOCIAL NETWORK MODELS FROM EGOCENTRICALLY SAMPLED DATA, WITH APPLICATION TO UNDERSTANDING PERSISTENT RACIAL DISPARITIES IN HIV PREVALENCE IN THE US.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets