Marginalization, Information, and Infection: Risk Behavior Correlation in Ghettoized Sociogeographic Networks and the Spread of Disease to Majority Populations

Publisher Website
Google Scholar
Abstract
Information theory analysis suggests that observed frequency distributions and intercorrelations of risk—benefit behaviors as determined from administrative data sets and surveys should characterize the internal structure of community-scale social networks, particularly in the context of social disintegration. This perspective further suggests that a ‘code’ is created using behaviors and correlated sequences of behaviors to overcome the noise generated by the oppressive externalities and consequent internal feedbacks causing community disorganization. The often-observed and highly destructive clustering of violence, substance abuse, and unsafe sexual activity appears as a consequence of a fundamental canonical mechanism (described by the Shannon—McMillan theorem) which favors intercorrelated meaningful sequences of behavioral or other signals to maximize the rate of transmission of information under noisy conditions. Such intercorrelation, in addition to accelerating the spread of infection carried by risk behaviors within marginalized communities, may cause their fragmented networks to become highly efficient compound channels, acting as bridges for the spread of human immunodeficiency virus (HIV) and other pathogens or behavioral pathologies from inner core groups to embedding majority populations. Individually oriented prevention programs addressing risk behaviors in ghettoized populations will thus have limited impact—particularly as strategies for the containment of infection—without a parallel address of the external marginalizing stressors which structure and enhance the value of such behaviors as useful symbols for communication. Without the introduction of new, politically based behaviors as alternative symbols within ‘core group’ communities, control of HIV and other pathogens seems unlikely in the United States.