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RunningHead: MONTHLY TRENDS IN CHLAMYDIA ANDGONORRHEA 1
Monthly Variation in Diagnosis Rates for Bacterial Sexually Transm...
MONTHLY TRENDS IN BACTERIALSTD 2
Abstract
The objective of this study is to determine if there is any pattern of variation...
MONTHLY TRENDS IN BACTERIALSTD 3
Sexually transmitted disease (STD) risk reduction and prevention education in the United
...
MONTHLY TRENDS IN BACTERIALSTD 4
pre-term labor, and increased risk for other STDs. Because of the largely asymptomatic na...
MONTHLY TRENDS IN BACTERIALSTD 5
social norms with unique routes of information acquisition which need to be considered wh...
MONTHLY TRENDS IN BACTERIALSTD 6
the rate of diagnosis for chlamydia and gonorrhea. The method remains the same, and the
d...
MONTHLY TRENDS IN BACTERIALSTD 7
variables include age, race, ethnicity, pregnancy status, date of event, region based on ...
MONTHLY TRENDS IN BACTERIALSTD 8
indicate which parameter the date of event represents.
Data were excluded from the study ...
MONTHLY TRENDS IN BACTERIALSTD 9
difference. For the college age group, however, the p-value of 0.003 indicates that the n...
MONTHLY TRENDS IN BACTERIALSTD 10
Table 1 Sample Characteristics with Odds Ratio for Health Department as Service Location...
MONTHLY TRENDS IN BACTERIALSTD 11
Figure 1 Event Distribution by Age
Figure 2 Mean Monthly Case Count by Age Group
0
50
10...
MONTHLY TRENDS IN BACTERIALSTD 12
Table 2 ANOVA ofMean Monthly Event Rates by Age Group
Age Group Sum of
Squares
df Mean
S...
MONTHLY TRENDS IN BACTERIALSTD 13
Table 4 ANOVA ofMean Seasonal Event Rates by Age Group
Age Group Sum of
Squares
df Mean
...
MONTHLY TRENDS IN BACTERIALSTD 14
Figure 3 Mean and Interquartile Range for Monthly Event Count: College Age Group
MONTHLY TRENDS IN BACTERIALSTD 15
Figure 4 Mean and Interquartile Range for Seasonal Event Count: College Age Group
MONTHLY TRENDS IN BACTERIALSTD 16
Discussion
This is the first study of its kind known to this investigator. There are imp...
MONTHLY TRENDS IN BACTERIALSTD 17
emergency and urgent care centers might also be a useful measure of the desire for eveni...
MONTHLY TRENDS IN BACTERIALSTD 18
gonorrhea cultures usually by in-house microscopy with a few specimens going to private ...
MONTHLY TRENDS IN BACTERIALSTD 19
promoting prevention and screening should target the College Age Group demographic befor...
MONTHLY TRENDS IN BACTERIALSTD 20
References
Aral, S. O., Fenton, K. A., & Holmes, K. K. (2007, June 18). Sexually transmi...
MONTHLY TRENDS IN BACTERIALSTD 21
Paschal, A. M., Oler-Manske, J., & Hsiao, T. (2011, July 24). The role of local health
d...
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Lisa Barnes PHC6946 Internship Paper

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Lisa Barnes PHC6946 Internship Paper

  1. 1. RunningHead: MONTHLY TRENDS IN CHLAMYDIA ANDGONORRHEA 1 Monthly Variation in Diagnosis Rates for Bacterial Sexually Transmitted Disease: A Five Year Cross-Sectional Study Lisa Miles Barnes PHC6946 Internship in Public Health University of West Florida December 10, 2014
  2. 2. MONTHLY TRENDS IN BACTERIALSTD 2 Abstract The objective of this study is to determine if there is any pattern of variation throughout the calendar year in the rate of diagnosis for bacterial sexually transmitted disease (STD) in Harnett County, North Carolina, which is a rural county with approximately 120,000 residents. Using data from the North Carolina Electronic Disease Surveillance System (NCEDSS), 2255 laboratory positive cases of chlamydia and gonorrhea in Harnett County residents age 13 to 61 were identified. Monthly case counts were analyzed by age group using Analysis of Variance (ANOVA) testing to determine variance between the mean case counts by month over a 57 month period. Age groups were coded by intervals: 13 to 18, 19 to 22, and ≥ 23. These ranges were chosen for their relevance to public health education and outreach planning. The college- age group (19-22) had a significantly higher mean event rate for the month of March, confirmed by Tukey Honestly Significant Difference (HSD) post-hoc testing. It is unclear whether this pattern reflects a variation in natural occurrence of disease or a rise in the rate of testing. Due to the highly asymptomatic nature of chlamydia and gonorrhea, additional studies are needed to reach a more specific conclusion about the cause of the variation. Until that time, it is not unreasonable to choose the months preceding the March peak as a potentially effective time to schedule community STD education, prevention, and risk reduction activities aimed at this age group.
  3. 3. MONTHLY TRENDS IN BACTERIALSTD 3 Sexually transmitted disease (STD) risk reduction and prevention education in the United States often takes place during the individual healthcare encounter. As changes in healthcare policy and structure pressure providers to see more patients in a shorter period of time, patient education suffers. Public health professionals have a unique opportunity to customize risk reduction and disease prevention efforts based on information collected during the surveillance tasks required by state laws. Local surveillance data give the most accurate picture of the burden of diseases within the community and their effects on specific demographic sectors. Patterns of disease incidence and prevalence within a community offer guidance for targeting public health education by health departments, especially important for those in rural areas which have strict limits on staff and funding resources for outreach and education. In 2011, the CDC estimates that 1.7 million people in the United States, approximately 560 per 100,000 persons in the total population, tested positive for chlamydia or gonorrhea (Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD and TB Prevention, 2011). Data consistently show that the burden of STD’s is greatest among those less than 24 years of age (Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD and TB Prevention, 2011) (Datta, et al., 2007) (Beydoun, Dail, Tamim, Ugwu, & Beydoun, 2010) (Paschal, Oler-Manske, & Hsiao, 2011), with this cohort representing 70% of chlamydia cases and 62% of gonorrhea cases (Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD and TB Prevention, 2011). The increased incidence rates are likely related to an increase in screening of asymptomatic patients for STD’s which seems to confirm the assertion that the majority of cases of chlamydia and gonorrhea are asymptomatic. Without diagnosis and treatment, people can experience serious sequelae such as pelvic inflammatory disease, ectopic pregnancies, infertility,
  4. 4. MONTHLY TRENDS IN BACTERIALSTD 4 pre-term labor, and increased risk for other STDs. Because of the largely asymptomatic nature of these infections, screening is often the only means of identifying and treating infection. Those who do not recognize or understand their risk for STD’s are not likely to seek screening services. For this reason, education and prevention are of the utmost importance. In the United States, the existing research overwhelmingly describes the highest rates of bacterial STDs among younger persons 14 to 19 years of age, and among black females, with the smallest numbers among whites 25 and over. Infection rates have consistently been reported highest among the black population (Paschal, Oler-Manske, & Hsiao, 2011) followed by Hispanic and whites, respectively (Fine, Thomas, Nakatsukasa-Ono, & Marrazzo, 2012) (Datta, et al., 2007) (Beydoun, Dail, Tamim, Ugwu, & Beydoun, 2010). One study shows the highest subpopulation using their STD services is non-Hispanic whites (Satterwhite, et al., 2011). Little, however, is known about patterns of disease transmission within the year that might exist for chlamydia and gonorrhea. Positivity rates among those tested has stayed relatively stable over the decade from 200-2010 (Satterwhite, et al., 2011) , but some experts caution that speculation about disease rates for diseases with a significant asymptomatic subpopulation is educated guesswork, at best (Miller & Siripong, 2013). The majority of studies which include chlamydia or gonorrhea disease rates in their research questions offer data from the National Health and Nutrition Examination Survey (NHANES) data as either the standard of comparison or as the source of data (Datta, et al., 2007), which necessarily causes the results of those studies to build upon the strengths and weaknesses of NHANES results. Identification of monthly or seasonal trends in disease incidence would guide program planning by indicating who might benefit from prevention education and at what point during the year might such education have the highest impact. Additionally, demographics like age group and race can be indications of cultural and
  5. 5. MONTHLY TRENDS IN BACTERIALSTD 5 social norms with unique routes of information acquisition which need to be considered when designing a health education program. No available studies addressed trends of disease incidence within the calendar year. Currently, North Carolina General Statute (Healthy Youth Act, 2009) describes the requirements for a health education program to be administered to students which includes contraceptive and sexually transmitted disease information with parental consent. It specifies that during seventh, eighth, and ninth grade, students shall be instructed about the biology of STDs and the fundamental concepts of disease transmission and prevention. Local health department (LHD) activities should complement and supplement this education rather than duplicating it. The question that must be answered is whether any intra-annual trend in the incidence rates of bacterial STDs exists. If the rate of disease increases during a certain month or season for certain groups, educators can schedule interventions and programs to precede peak activity and attempt to prevent disease before it occurs. After receiving and exploring the available data, its method of collection and its true denominator, it became obvious that the original research question could not be adequately answered by this data. In order to determine a monthly or seasonal trend in the natural occurrence of disease, the data would need to represent the date of transmission. As discussed previously, likely more than half of chlamydia cases have had no symptoms and do not seek testing until some other purpose causes them to seek STD testing. In those who do experience symptoms, the interval between exposure and symptom onset can vary by as much as several weeks (Centers for Disease Control and Prevention, 2012). What the data actually can reveal, then, is the date on which patients received STD testing and subsequent diagnosis. The revised research question, then, must be whether there is an intra-annual trend in
  6. 6. MONTHLY TRENDS IN BACTERIALSTD 6 the rate of diagnosis for chlamydia and gonorrhea. The method remains the same, and the discussion section addresses how the answer to this new question can be used to tailor public health services and education for the greatest effect in preventing, diagnosing, and treating these infections. The data also inspire ideas and recommendations for future studies which can move toward answering the original research question. A cross-section study is most useful to determine the prevalence of a condition within a study population and the odds ratios for the independent variables within the sample. The condition of interest studied here is the month in which the diagnosis of chlamydia or gonorrhea takes place and whether there is a difference between the months of the year regarding the rate of case identification. Method Retrospective data representing all laboratory-positive chlamydia and gonorrhea as reported to the LHD between April 1, 2008 and December 31, 2012 were received for this study. Data were extracted from the North Carolina Electronic Disease Surveillance System (NCEDSS), a passive surveillance system used by all LHD’s in North Carolina for collecting reportable disease data in compliance with statutory requirement NCGS §130A-135 (1983). LHD staff checked the data for completeness, and data were de-identified prior to the commencement of the study. The Public Health Education supervisor and the Director of Nursing reviewed the data set and determined that the data meet the requirements for exemption from IRB approval. Data were received as a spreadsheet and were imported to SPSS (IBM, v22.0) for analysis. Variables were described and recoded as categorical data with the exception of age which was both maintained as nominal data and recoded into relevant categories. Independent
  7. 7. MONTHLY TRENDS IN BACTERIALSTD 7 variables include age, race, ethnicity, pregnancy status, date of event, region based on zip codes, reporter (type of health care service provider providing the report to the health department) and a bivariate indicator of whether the individual patient appeared more than once in the data set. Data collected in NCEDSS represent cases of confirmed laboratory positive chlamydia and gonorrhea in persons whose stated current address at the time of specimen collection is within Harnett County, North Carolina, and which were reported to the health department by the ordering provider or by the lab conducting the ordered test. Reports are received by telephone, by fax, and by electronic transfer from certain laboratories and entered into the system by trained health department personnel, usually nurses. Cases are then reviewed by Department of Health staff at the state level for comparison against the case definition. The study period of 57 months begins on April 1, 2008 due to the adoption of electronic case reporting with case entry required beginning on that date. According to Rob Pace, RN, Acting NCEDSS Lead (Telephone Interview: February 6, 2014), cases were documented on both paper and electronic media for several months to allow for data entry training, but all cases were entered retroactively to April 1 from the paper reports filed on or after that date. Concerns about bias based on this major change in reporting procedures and the labor intensive process that would be required to extract similar data from paper-based archives resulted in the decision to begin the study period on April 1 in place of the original plan for January 1, 2008. The term ‘event’ is used in NCEDSS to identify a unique person-diagnosis case. The date of the event is defined as the ‘best date of identification’ for the event. If a symptom onset date was reported with the data from the ordering provider, that date is assigned as a truer indication of the event date. If a symptom onset was not given or if the test was done as a screening, the date of specimen collection is used. The data set used for this study does not
  8. 8. MONTHLY TRENDS IN BACTERIALSTD 8 indicate which parameter the date of event represents. Data were excluded from the study for missing age (n=1) and for age outside the study range of 13 to 70 years of age (n=2). The total study population of 2255 participants includes 409 cases in males and 1846 in females. The mode for age is seventeen years old with a range from 13 to 61 (Figure 1). Age groups identified for this study represent groups which require different strategies for outreach and education based on potential participation in traditional school structure. The secondary school group is defined as 13 to 18 years of age, and the college group as 19 to 22. Participants over the age of twenty-two are combined into an adult category. Descriptive statistics for each of the demographic variables along with the odds ratio (OR) of using the LHD for STD testing services appears in Table 1. Table 2 shows the distribution of cases by month over the study period. Analysis of Variance (ANOVA) was used to determine whether there is a significant difference between the mean event counts by month of the year over the 57 month period. This is repeated after separating the data by the categorical age cohorts described previously. For results with p< 0.05, the Tukey Honestly Significant Difference (HSD) test is performed to determine which pairings of data reach the level of significant difference for the mean case counts (Table 3). The ANOVA is then repeated, replacing the time period Month with Season (Table 4). Seasons were defined as Spring (March-May), Summer (June-August), Fall (September-November) and Winter (December-January). Post-hoc Tukey HSD test results are shown in Table 5. Results ANOVA for the population as a whole revealed no statistically significant difference between the mean event rates by month. In the second round of tests the mean event rates for the high school age group (13 to 18) and the adult group (23 and older) also had no significant
  9. 9. MONTHLY TRENDS IN BACTERIALSTD 9 difference. For the college age group, however, the p-value of 0.003 indicates that the null hypothesis is rejected at the α = 0.05 level. Recall that in the null hypothesis there is no difference between the mean event counts for chlamydia and gonorrhea throughout the year. Since there is a significant difference for one subgroup of the population, the next step is to determine which month(s) contain the mean(s) which create that difference. The Tukey HSD test paired all the months against each other in sequence to determine which pairs were statistically different. The results are shown in Table 3. All of the paired months for which p is less than 0.05 contain March. Therefore, March must be significantly different. Figure 2 offers a clear picture of this trend. There were 3 months which, when paired with March, did not meet the level of significance and those were March-November (p=0.423), March-April (p=0.176), and March-September (p=0.066). In the second set of analyses, a similar pattern was detected by season. No significant differences were seen in the mean case counts by season for the population as a whole or for the High School Age Group or Adult Age Group. In the College Age Group there was, again, a significant difference among the means. Spring had the highest mean and Fall had the lowest. Tukey HSD (Table 5) shows a significance between pairs Spring-Summer (p=0.018) and Spring- Winter (p=0.011). See Figures 3 and 4 for visual representation of the difference between the means for both studies.
  10. 10. MONTHLY TRENDS IN BACTERIALSTD 10 Table 1 Sample Characteristics with Odds Ratio for Health Department as Service Location Indicators n Prevalence (%) Odds Ratio (95% CI) Age Category 13 to 18 596 26.4 **0.663 0.483-0.830 19 to 22 847 37.6 **0.762 0.602-0.966 ≥ 23 812 36.0 -- -- Sex Male 409 18.1 ***2.340 1.666-3.284 Female 1846 81.9 -- -- Pregnancy Status Pregnant 301 13.3 0.709 0.464-1.083 NotPregnant 1024 45.4 ***2.167 1.636-2.869 Unknownor MissingData 521 23.1 -- -- NotApplicable/Male 409 18.1 -- -- Race White 511 22.7 **1.554 1.084-2.229 Black 1144 50.7 **1.612 1.143-2.275 Asian 9 0.4 1.029 0.124-8.557 PacificIslander 4 0.2 5.796 0.552-60.839 Native American/Alaskan 6 0.3 3.197 0.603-16.951 Other 28 1.2 -- -- Unknownor Missing 552 24.5 -- -- Hispanic Ethnicity Yes 93 4.1 ***2.805 1.641-4.794 No 1201 53.3 **1.386 1.062-1.809 Unknownor Missing 961 42.6 -- -- Regionby Zip Code 27326-27339 198 8.8 **2.269 1.528-3.369 27501-27543 400 17.7 **1.596 1.146-2.224 27546-27592 584 25.9 **5.025 3.811-6.625 28323-28326 296 13.1 **2.968 2.126-4.145 28334-28390 777 34.5 -- -- **p<0.05, ***p<0.001
  11. 11. MONTHLY TRENDS IN BACTERIALSTD 11 Figure 1 Event Distribution by Age Figure 2 Mean Monthly Case Count by Age Group 0 50 100 150 200 250 300 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 Event Count Age n = 2255 0 10 20 30 40 50 60 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Mean Event Count Month Total Sample ≥ 23 19-22 13 to 18
  12. 12. MONTHLY TRENDS IN BACTERIALSTD 12 Table 2 ANOVA ofMean Monthly Event Rates by Age Group Age Group Sum of Squares df Mean Square F p-value All 1792.809 11 162.983 1.173 0.332 Within groups 6250.700 45 138.904 13 to 18 232.190 11 21.108 1.224 0.299 Within groups 775.950 45 17.243 19 to 22 833.146 11 75.741 3.243 0.003 Within groups 1051.100 45 23.358 ≥ 23 218.211 11 19.837 0.711 0.722 Within groups 1256.350 45 27.919 Table 3 Tukey HSD for ANOVA: Month Pairs with March, College Age Group Mean Difference Std. Error Sig. 95% Confidence Interval Lower Upper MAR JAN 14.250 3.417 0.007 2.480 26.020 FEB 13.250 3.417 0.016 1.480 25.020 APR 9.400 3.242 0.176 -1.770 20.570 MAY 11.400 3.242 0.042 0.230 22.570 JUN 13.400 3.242 0.008 2.230 24.570 JUL 12.600 3.242 0.015 1.430 23.770 AUG 14.400 3.242 0.003 3.230 25.570 SEP 10.800 3.242 0.066 -0.370 21.970 OCT 14.800 3.242 0.002 3.630 25.970 NOV 7.800 3.242 0.423 -3.370 18.970 DEC 14.600 3.242 0.002 3.430 25.770
  13. 13. MONTHLY TRENDS IN BACTERIALSTD 13 Table 4 ANOVA ofMean Seasonal Event Rates by Age Group Age Group Sum of Squares df Mean Square F p-value All 791.969 3 263.990 1.929 0.136 Within groups 7251.540 53 136.822 13 to 18 52.466 3 17.489 0.970 0.414 Within groups 955.674 53 18.032 19 to 22 378.427 3 126.142 4.440 0.007 Within groups 1505.818 53 28.412 ≥ 23 105.050 3 35.017 1.355 0.267 Within groups 1369.512 53 25.840 Table 5 Tukey HSD for ANOVA Season Pairs: College Age Group Mean Difference Std. Error Sig. 95% Confidence Interval Lower Upper Spring Summer 6.038 1.981 0.018 0.78 11.29 Fall 3.705 1.981 0.253 -1.55 8.96 Winter 6.648 2.053 0.011 1.20 12.09 Summer Spring -6.038 1.981 0.018 -11.29 -0.78 Fall -2.333 1.946 0.630 -7.50 2.83 Winter 0.610 2.020 0.990 -4.75 5.97 Fall Spring -3.705 1.981 0.253 -8.96 1.55 Summer 2.333 1.946 0.630 -2.83 7.50 Winter 2.944 2.020 0.470 -2.41 8.30 Winter Spring -6.648 2.053 0.011 -12.09 -1.20 Summer -0.610 2.020 0.990 -5.97 4.75 Fall -2.944 2.020 0.470 -8.30 2.41
  14. 14. MONTHLY TRENDS IN BACTERIALSTD 14 Figure 3 Mean and Interquartile Range for Monthly Event Count: College Age Group
  15. 15. MONTHLY TRENDS IN BACTERIALSTD 15 Figure 4 Mean and Interquartile Range for Seasonal Event Count: College Age Group
  16. 16. MONTHLY TRENDS IN BACTERIALSTD 16 Discussion This is the first study of its kind known to this investigator. There are implications for program and public health education planning for Harnett County Health Department (HCHD) managers who hope to reduce the burden of chlamydia and gonorrhea among its most susceptible residents. Community education projects and outreach events can be tailored to match the developmental level of the target audience. Data and results shown by this study can help prioritize target age groups and timing of interventions for disease prevention. Action plans based on the results given here must first take the strengths and limitations of the study into account. The study gets its robustness from the complete population used as the study sample. No additional sampling was done after data cleaning to identify the study sample from the original data set supplied. The results of the analysis were highly significant at the 95% confidence level, lending greater credence to the outcomes. These results could be influenced by multiple covariants, some of which point us toward the next steps in analyzing these incidence rates for the purpose of program planning. First, the data do not represent the date of STD transmission or, in many cases, the onset of symptoms. The delay between transmission and testing could cause the event date to move to a different month or season. In the cases which do not represent symptom onset, they represent the date the patient arrived in the health care system for testing. Truer results of natural disease incidence patterns would require either consistently recording a symptom onset date if one exists, or information about the length of time a patient had to wait for an appointment to be tested from the date of the request. A desire for same day testing is perhaps a motivator for some of the patients who used hospital emergency rooms and urgent care centers for testing. The use of
  17. 17. MONTHLY TRENDS IN BACTERIALSTD 17 emergency and urgent care centers might also be a useful measure of the desire for evening and weekend testing. Additional studies are needed to determine the distribution of cases among the different reporter categories. Inclusion in this data set required that the patient’s stated address was within the geographical bounds of Harnett County on the date of the clinical encounter for the event. Many health care providers rely on self-reporting or fail to update records. Inaccurate address data could cause cases to be included which rightly fell under another jurisdiction, and could result in cases belonging to Harnett County to be counted elsewhere. To the extent that neighboring counties and counties from which students travel to come to colleges in Harnett County, the results of the study would be skewed toward the trends for the subject’s county of origin. Cases may be under-reported by some health care providers. NCEDSS is a passive surveillance system which relies on the provider to initiate reporting. To counteract this potential confounder, North Carolina Department of Public Health has worked with some of the larger laboratories in the area to achieve interoperability of electronic records and arrange for automatic reporting of notifiable diseases without human intent or action. This type of reporting has been in place throughout the study period for specimens processed at the North Carolina State Lab for Public Health (SLPH) and through LabCorp, with major hospital labs and other private labs reporting often by telephone or fax. This creates a potential for bias toward cases tested at the locations with automatic uploading into NCEDSS. The most concerning potential for under-reporting is based on the lack of chlamydia testing in males. The SLPH does not offer processing of any test for chlamydia or gonorrhea collected from males. A large number of the clients who use the LHD for testing do not have healthcare coverage to help with the cost of testing in a private laboratory. HCHD processes
  18. 18. MONTHLY TRENDS IN BACTERIALSTD 18 gonorrhea cultures usually by in-house microscopy with a few specimens going to private labs at cost. Males who are treated at any location for chlamydia as a contact to a known case are not reported due to the lack of laboratory confirmation. If the male sub-population experiences a different pattern of disease identification, it could skew the distribution for the population taken as a whole. This study did not represent variations in event date based on gender. NCEDSS treats co-infection with chlamydia and gonorrhea as two events. This might lead to a redundancy error. Although NCEDSS is able to recognize and merge events for the same patient who tests at multiple locations within a short period of time, any two diagnoses for the same condition are treated as separate events if the specimen dates are greater than thirty days apart. This, too, might create a redundancy error. For clusters of positive results around school vacation times, several hypotheses present themselves. Patients might seek testing services during school breaks due to fewer constraints on their time during that period. They might also be participating in high risk sexual behaviors during their vacations and quickly pursue STD testing after considering the exposure risks they have created. This pattern is a concern as it does not allow for an incubation period and might produce false negative tests due to very recent infection. More data are needed to determine if there is a true increase in the frequency of STD testing during the Spring period. The data for this study did not include any information about negative test results or total number of tests performed. If testing is more frequent during this time due to free time to seek an appointment, clinics should consider offering alternative appointment times, on evenings or weekends, especially in late winter or early spring. Alternately, college age patients could be given priority appointments during this period or additional staff could be pulled from other departments to assist with the increased demand. Advertising, education, and public service announcements
  19. 19. MONTHLY TRENDS IN BACTERIALSTD 19 promoting prevention and screening should target the College Age Group demographic before and during the anticipated peak period, as well. If the increased event rate or testing rate is related to a change in sexual behaviors, education and prevention efforts can be tailored to address STDs and related topics such as safety, coercion, and substance abuse. Conclusion This study represents the first step in increasing understanding of the incidence and prevalence of chlamydia and gonorrhea in Harnett County, North Carolina. While there are several potential confounders, the significance level of the results warrants intervention as well as further investigation. The March peak for bacterial STDs in the College Age Group can reasonably be used as a guide to schedule and design community education and prevention program goals to reduce the burden of these infections in the population. Further studies on the same data set will be useful in estimating the potential benefit of adding evening and weekend clinic hours and to increase overall clinic capacity to reduce the delay between appointment scheduling and appointment time.
  20. 20. MONTHLY TRENDS IN BACTERIALSTD 20 References Aral, S. O., Fenton, K. A., & Holmes, K. K. (2007, June 18). Sexually transmitted diseases in the USA: Temporal trends. Sexually Transmitted Infections, 83, 257-266. doi:10.1136/sti.2007.026245 Beydoun, H. A., Dail, J., Tamim, H., Ugwu, B., & Beydoun, M. A. (2010, November 12). Gender and age disparities in the prevalence of chlamydia infection among sexually active adults in the United States. Journal of Women's Health, 19(12), 2183-2190. doi:10.1089/jwh.2010.1975 Blatt, A. J., Lieberman, J. M., Hoover, D. R., & Kaufman, H. W. (2012, July). Chlamydial and gonococcal testing during pregnancy in the United States. American Journal of Obstetrics and Gynecology, 207(1), 55.e1-55.e8. doi:10.1016/j.ajog.2012.04.027 Centers for Disease Control and Prevention. (2012, January 7). CDC fact sheet. Retrieved November 12, 2014, from Chlamydia: http://www.cdc.gov/std/chlamydia/STDFact- Chlamydia-detailed.htm Centers for Disease Control and Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD and TB Prevention. (2011). STD trends in the United States: 2011 national data for chlamydia, gonorrhea, and syphilis. Retrieved March 12, 2014, from Centers for Disease Control and Prevention: http://www.cdc.gov/std/stats11/trends Datta, S. D., Sternberg, M., Johnson, R. E., Berman, S., Papp, J. R., McQuillan, G., & Weinstock, H. (2007, July 17). Gonorrhea and chlamydia in the United States among persons 14 to 39 years of age, 1999 to 2002. Annals of Internal Medicine, 147(2), 89-96, 122. Retrieved March 12, 2014, from www.annals.org Fine, D., Thomas, K. K., Nakatsukasa-Ono, W., & Marrazzo, J. (2012, Jan-Feb). Chlamydia positivity in women screened in family planning clinics: Racial/ethnic difference and trends in the northwest U.S., 1997-2006. Public Health Reports, 127(1), 38-51. Retrieved March 12, 2014 Harnett County Health Department. (2008-2012). Deidentified Event Listing by Creation Date. [Data file]. Retrieved from https://ncedss.ncpublichealth.com Healthy Youth Act, NCGS S115C-81 (2009). Miller, W. C., & Siripong, N. (2013, March). Estimates of secually transmitted infection prevalence and incidence in the United States: Time to embrace uncertainty. Sexually Transmitted Diseases, 40(3), 194-196. doi:10.1097/OLQ.0b013e318286dba6
  21. 21. MONTHLY TRENDS IN BACTERIALSTD 21 Paschal, A. M., Oler-Manske, J., & Hsiao, T. (2011, July 24). The role of local health departments in providing sexually transmitted disease services and surveillance in rural communities. Journal of Community Health, 36, 204-210. doi:10.1001/s10900-010-9298-6 Physicians to Report, NCGS S130A-135 (1983). Satterwhite, C. L., Grier, L., Patzer, R., Weinstock, H., Howards, P. P., & Kleinbaum, D. (2011, November). Chlamydia positivity trands among women attending family planning clinics: United States, 2004-2008. Sexually Transmitted Diseases, 38(11), 989-994. doi:10.1097/OLQ.0b013e318225f7d7

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