Award Date


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Environmental and Occupational Health

First Committee Member

Mark Buttner

Second Committee Member

Patricia Cruz

Third Committee Member

Timothy Bungum

Fourth Committee Member

Daniel Young

Number of Pages



Within the spectrum of childhood (infancy, early childhood, middle childhood, and adolescence), TB is a priority disease, and preventive care is recommended by the American Academy of Pediatrics (AAP)/Bright Futures initiative (AAP, 2008). Within the early childhood stage (15 months to 4 years), TB is classified as "HR2" (high risk 2), behind lead poisoning, which is "HR1" (AAP, 2008), the highest risk within this age range. Within the middle childhood stage (5 to 10 years), TB becomes an HR1. TB is preventable, and targeted screening is the best prevention method. The AAP/Bright Futures initiative further specifies the risk based on pediatric contacts, such as household members/close contacts.

From 2008 to 2012, Nevada led the nation in pediatric TB with 5.7 cases per 100,000 in the 1 to 4 year-old age range (OTIS, 2014). In 2010, the Nevada State Health Division recognized the following pediatric TB risk factors (Paulson, 2010): many of these cases are children of young mothers, or are young mothers themselves; individuals who have spent time in jails, detention centers and prisons have been identified as contacts to these pediatric TB cases; most of the cases (especially less than 5 years of age) had recent interactions with healthcare providers prior to being diagnosed with TB; and country of birth of pediatric contacts is a risk factor.

The objective of this study was to create a social network model (Hanneman, 2005) and perform associated social network analysis to evaluate tuberculosis case and contact investigation data in Clark County, Nevada. The social network model was then used to assess pediatric disease transmission based on network metrics and individual risk factors. Social network analysis was used to assess pediatric TB transmission based on links between pediatric cases and contacts in Clark County, Nevada for the years 2010, 2011, and 2012. Network metrics were used to establish locational properties of cases and contacts, and through incorporation of individual risk factors disease transmission potential was established. Whole-network, group network and individual network metrics and risk factors provided areas of focus for prevention, treatment, prophylaxis, control, and case management of pediatric TB cases.

The Wilcoxon signed-rank test, Kruskal-Wallis test, logistic regression, and logistic regression with bootstrapping were used to calculate the significance of the risk factors and network metrics. The TB network, as a whole, was stable and relatively static from 2010 to 2012 based on density and clustering coefficient; however, at the individual and group levels there were focal areas that were more dynamic. The risk factors identified by the Nevada State Health Division varied in terms of significance, with significance demonstrated only by logistic regression and logistic regression with bootstrapping. Although social network analysis has limitations it can be useful as a complementary method for on-going surveillance of pediatric TB that can improve health outcomes with targeted and cost-effective interventions, and can influence public health policy. Some advantages of TB infectious disease modeling are: better resource allocation, improved contact investigation efficiency, prioritized treatment, education, and improved Directly Observed Treatment Short-Course (DOTS) therapy.


Communicable diseases in children; Pediatric; Social network analysis; Social sciences – Network analysis; Tuberculosis; Tuberculosis – Prevention


Infectious Disease | Maternal and Child Health | Pediatrics | Public Health

File Format


Degree Grantor

University of Nevada, Las Vegas




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