Chlamydia Infections in Pinellas County, Essay Example

Secondary Data Source for Chlamydia Infections in Pinellas County, Florida

Secondary data is beneficial when conducting epidemiological or cross-sectional studies in which larges datasets need to be analyzed (Waltz, Strickland, & Lenz, 2010; Bauman & Nutbeam, 2013). Such data allows for researchers to obtain a glimpse of a particular phenomenon at one point in time across an abundance of subjects, producing a high degree of confidence in the results (Robson, 2011; Ziebland & Hunt, 2014). For a final project regarding indicators contributing to the need for repeat treatment for Chlamydia infection in Pinellas County, Florida in 2013/4, a secondary data source that may prove to be useful is The Florida Health Department’s (FHD) county comparison of sexually transmitted disease rates (STD, 2014). This department maintains accurate yearly records of common STDs by county, as well as demographic analyses of specific STD cases (e.g., prevalence by gender and age). Data collected for the FHD is believed to be reliable, as well as the most accurate source of information available regarding Chlamydia infections in specific counties within Florida. Due to its breakdown of demographic variables, this data source will be helpful in identifying contributors to repeat treatments. By combining information from the FHD with rates of treatment resistance or relapse, a predictive risk model could be developed explaining the relationship between key variables and repeat treatments (Shoukri & Cihon, 2010; Polgar & Thomas, 2013). The ability to examine trends in data across large sample sizes, such as those included within the FHD’s report, will significantly increase the statistical power achieved when analyzing results, thus, greatly increasing the external validity and generalizability of this study to the larger population (Cohen, 2013; Cresswell, 2013). Specifically, these results will allow for conclusions to be made about contributors to repeat treatment for Chlamydia infection in both Pinellas County, Florida and other counties throughout the country (Falasinnu et al., 2014).

References

Bauman, A., & Nutbeam, D. (2013). Evaluation in a nutshell: a practical guide to the evaluation of health promotion programs. New York: McGraw Hill.

Cohen, J. (2013). Statistical power analysis for the behavioral sciences. London: Routledge Academic.

Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. London: Sage.

Falasinnu, T., Gustafson, P., Hottes, T. S., Gilbert, M., Ogilvie, G., & Shoveller, J. (2014). A critical appraisal of risk models for predicting sexually transmitted infections. Sexually Transmitted Diseases, 41(5), 321-330.

Florida Health (2014). STD trends and statistics. Retrieved from: http://www.floridahealth.gov/diseases-and-conditions/sexually-transmitted-diseases/std-statistics/. Accessed 3 Oct. 2014.

Polgar, S., & Thomas, S. A. (2013). Introduction to research in the health sciences. Amsterdam: Elsevier Health Sciences.

Robson, C. (2011). Real world research: a resource for users of social research methods in applied settings. Chichester: Wiley.

Shoukri, M. M., & Cihon, C. (2010). Statistical methods for health sciences. Boca Raton, FL: CRC Press.

Waltz, C. F., Strickland, O., & Lenz, E. R. (2010). Measurement in nursing and health research. New York: Springer Pub.

Ziebland, S., & Hunt, K. (2014). Using secondary analysis of qualitative data of patient experiences of health care to inform health services research and policy. Journal of Health Services Research & Policy, 19(3), 177-182.