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Predicting incidence and asymptomatic rates for chlamydia in small domains

Authors


T.L. Thomas: e-mail: tami.thomas@emory.edu

Abstract

thomas t.l. & nandram b. (2010) Predicting incidence and asymptomatic rates for chlamydia in small domains. Journal of Advanced Nursing66(12), 2650–2658.

Abstract

Aim.  This article is a report of a study of the use of predictive analysis using the Bayesian hierarchical model and small area estimation as an innovative methodology to address the challenges nurses face when managing fiscal and clinical resources in outpatient and inpatient settings.

Background.  Nurses responsible for clinic management are confronted with the fiscal challenges in today’s healthcare environment. Identifying those at risk for asymptomatic infections such as chlamydia and getting resources to that group has been a constant nursing care challenge for those in outpatient and inpatient clinics.

Methods.  A secondary analysis of quantitative survey data was conducted in 2008, using predictive analysis with the Bayesian hierarchical model and small area estimation of statistics.

Discussion.  The development of an innovative statistical procedure is an interesting and challenging opportunity. The opportunity to apply this innovative technique to an actual data set opens the possibility to replicating the technique and using it in other settings. If implemented and replicated, this innovative analysis can become a tool for managing limited fiscal and clinical resources.

Results.  White, Hispanic and African American undergraduate students had slightly higher rates than the corresponding graduate students. The incidence rates were higher for White, Hispanic and African American undergraduate students than for graduate students. The incidence rates for African Americans were much higher than for the other identified racial groups, but very similar for graduate students and undergraduate students.

Conclusion.  Predictive analysis using the Bayesian hierarchal model and small area estimation can help nurses to project healthcare costs and services for underserved groups in healthcare clinics, with an improved empirical rationale.

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