Evaluating the Effectiveness of Targeted Public Health Control Strategies for Chlamydia Transmission in Omaha, Nebraska: A Mathematical Modeling Approach

K. M. Monirul Islam, Ozgur M. Araz

Department of Epidemiology, Social & Behavioral Health College of Public Health, University of Nebraska Medical Center, Omaha, USA.
Department of Health Promotion, Social & Behavioral Health College of Public Health, University of Nebraska Medical Center, Omaha, USA.

DOI: 10.4236/aid.2014.43021                

Abstract

Objectives: Sexually Transmitted Infections (STIs) have a great public health impact globally. STIs are one of the most critical health problems in the United States of America (USA). Here, we present a mathematical model for testing several interventions that are designed for various communities in order to control the Chlamydia epidemic. Study Design: Based on a community sexual behavior survey, we constructed and parameterized a mathematical disease transmission model to estimate the spread dynamics of Chlamydia in young adults in the northern part of Omaha, Nebraska. Methods: A differential equations based continuous time simulation model is run for various scenarios. The model considers only one age group i.e., 19 - 25 ages, which is considered as the highest risk group for this sexually transmitted disease. Our model assumes homogeneous mixing within this age group and use published estimates to model mixing rates between individuals. Results: The presented model quantified the potential value of screening and treatment programs for Chlamydia in reducing the burden of disease in this specific community. By increasing the screening and treatment rates from 35% to 85%, great public health benefit can be achieved in two years, i.e., total cases reduction around 9% just in this considered age group. Conclusions: Computational results show that behavioral change based interventions on prevention have some effect on reducing the prevalence in the targeted age group; however, more benefit can be obtained with frequent screening and treatment programs.

Keywords

Chlamydia, Mathematical Modeling, Screening, Public Health, Policy

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Centers for Disease Control and Prevention (2012) Sexually Transmitted Disease Surveillance 2011. Centers for Disease Control and Prevention, Atlanta.
[2]	Heymann, D.L. (Ed.) (2008) Control of Communicable Diseases Manual. 19th Edition, APHA, Washington DC.
[3]	Wasserheit, J.N. (1992) Epidemiological Synergy: Interrelationships between Human Immunodeficiency Virus Infection and Other Sexually Transmitted Diseases. Sexually Transmitted Diseases, 19, 61-77. http://dx.doi.org/10.1097/00007435-199219020-00001
[4]	Douglas County Health Department, 2012. http://www.douglascountyhealth.com/images/stories/st- ats/morbidity/STD2012_short%20Compatibility%20Mode.pdf
[5]	STDs in Douglas County. Douglas County Health Department, 2012.
[6]	Region Population Summary by Race/Ethnicity. Douglas County Health Department, 2011.
[7]	U.S. Census Data, 2010. http://www.census.gov/population/www/cen2010/glance/
[8]	Althaus, C.L., Heijne, J.C.M., Roellin, A. and Low, N. (2010) Transmission Dynamics of Chlamydia trachomatis Affect the Impact of Screening Programmes. Epidemics, 2, 123-131. http://dx.doi.org/10.1016/j.epidem.2010.04.002
[9]	Heijne, J.C.M., Althaus, C.L., Herzog, S.A., Kretzschmar, M. and Low, N. (2011) The Role of Reinfection and Partner Notification in the Efficacy of Chlamydia Screening Programs. The Journal of Infectious Diseases, 203, 372-377.
[10]	Garnett, G.P., Mertz, K.J., Finelli, L., Levine, W.C. and St Louis, M.E. (1999) The Transmission Dynamics of Gonorrhoe: Modeling the Reported Behavior of Infected Patients from Newark, New Jersey. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 354, 787-797.
[11]	de Vries, R., van Bergen, J.E.A.M., Berh, L.T.W. and Postma, M. (2006) Systemic Screening for Chlamydia trachomatis: Estimating the Cost-Effectiveness Using Dynamic Modeling and Dutch Data. Value in Health, 9, 1-11. http://dx.doi.org/10.1111/j.1524-4733.2006.00075.x
[12]	Bansal, S., Grenfell, B.T. and Meyers, L.A. (2007) When Individual Behavior Matters: Homogenous and Network Models in Epidemiology. Journal of the Royal Society Interface, 4, 879-891. http://dx.doi.org/10.1098/rsif.2007.1100
[13]	Kretzschmar, M., van Duynhoven, Y.T.H.P. and Severijnen, A.J. (1996) Modeling Prevention Strategies for Gonorrhea and Chlamydia Using Stochastic Network Simulations. American Journal of Epidemiology, 144, 306-317. http://dx.doi.org/10.1093/oxfordjournals.aje.a008926
[14]	Owusu-Edusei, K., Gift, T., Chesson, H.W. and Kent, C.K. (2013) Investigating the Potential Public Health Benefit of Jail-Based Screening and Treatment Programs for Chlamydia. American Journal of Epidemiology, 177, 463-473. http://dx.doi.org/10.1093/aje/kws240
[15]	Regan, D.G., Wilson, D.P. and Hocking, J.S. (2008) Coverage Is the Key for Effective Screening of Chlamydia trachomatis in Australia. Journal of Infectious Diseases, 198, 349-358. http://dx.doi.org/10.1086/589883
[16]	Kretzschmar, M., Welte, R., van den Hoek, A. and Postma, M.J. (2001) Comparative Model-Based Analysis of Screening Programs for Chlamydia trachomatis Infections. American Journal of Epidemiology, 153, 90-101. http://dx.doi.org/10.1093/aje/153.1.90
[17]	Turner, K.M.E., Adams, E.J., Gay, N., Ghani, A.C., Mercer, C. and Edmunds, W.J. (2006) Developing a Realistic Sexual Network Model of Chlamydia Transmission in Britain. Theoretical Biology and Medical Modelling, 3, 3. http://dx.doi.org/10.1186/1742-4682-3-3
[18]	Althaus, C.L., Turner, K.M.E., Schmid, B.V., Heijne, J.C.M., Kretzschmar, M. and Low, N. (2012) Transmission of Chlamydia trachomatis through Sexual Partnership: A Comparison between Three Individual-Based Models and Empirical Data. Journal of the Royal Society Interface, 9, 136-146. http://dx.doi.org/10.1098/rsif.2011.0131
[19]	Ong, J.B.S., Fu, X., Lee, G.K.K. and Chen, M.I. (2012) Comparability of Results from Pair and Classical Model Formulations for Different Sexually Transmitted Infections. PLoS ONE, 7, e39575.
[20]	Tao, G., Zhao, K., Gift, T., Qiu, F. and Chen, G. (2012) Using a Resource Allocation Model to Guide Better Local Sexually Transmitted Disease Control and Prevention Programs. Operations Research for Health Care, 1, 23-29. http://dx.doi.org/10.1016/j.orhc.2012.05.001
[21]	Townshed, J.R.P. and Turner, H.S. (2000) Analyzing the Effectiveness of Chlamydia Screening. The Journal of the Operational Research Society, 51, 812-824. http://dx.doi.org/10.1057/palgrave.jors.2600978
[22]	Evenden, D., Harper, P.R., Brailsford, S.C. and Harinda, V. (2006) Improving the Cost Effectiveness of Chlamydia Screening with Targeted Screening Strategies. Journal of the Operational Research Society, 57, 1400-1412. http://dx.doi.org/10.1057/palgrave.jors.2602134
[23]	Diwekar, U.M. (2003) Introduction to Applied Optimization, Applied Optimization, Vol. 80. Kluwer Academic Publishers, The Netherlands. http://dx.doi.org/10.1007/978-1-4757-3745-5
[24]	Araz, O.M., Lant, T., Fowler, J.W. and Jehn, M. (2011) A Simulation Model for Policy Decision Analysis: A Case of Influenza Pandemic on a University Campus. Journal of Simulation, 5, 89-100. http://dx.doi.org/10.1057/jos.2010.6
[25]	Powersim Software AS (2003). http://www.powersim.com