Lisa Barnes PHC6946 Internship Paper

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

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.

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,

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

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

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

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

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

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.

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

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

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

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

Figure 3 Mean and Interquartile Range for Monthly Event Count: College Age Group

Figure 4 Mean and Interquartile Range for Seasonal Event Count: College Age Group

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

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

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

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.

References
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