lisa barnes phc6946 internship paper

Running Head: MONTHLY TRENDS IN CHLAMYDIA AND GONORRHEA 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 BACTERIAL STD 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 Category13 to 18 596 26.4 **0.663 0.483-0.83019 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 StatusPregnant 301 13.3 0.709 0.464-1.083

Not Pregnant 1024 45.4 ***2.167 1.636-2.869Unknown or Missing Data 521 23.1 -- --

Not Applicable/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

Pacific Islander 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 -- -- Unknown or 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

Unknown or Missing 961 42.6 -- --Region by Zip Code

27326-27339 198 8.8 **2.269 1.528-3.36927501-27543 400 17.7 **1.596 1.146-2.22427546-27592 584 25.9 **5.025 3.811-6.62528323-28326 296 13.1 **2.968 2.126-4.14528334-28390 777 34.5 -- --

**p<0.05, ***p<0.001


Figure 1 Event Distribution by Age

13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 610

50

100

150

200

250

300n = 2255

Age

Event Count

Figure 2 Mean Monthly Case Count by Age Group

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC0

10

20

30

40

50

60

Total Sample

≥ 23

19-22

13 to 18

Month

Mean Event Count


Table 2 ANOVA of Mean Monthly Event Rates by Age Group

Age Group Sum of Squares

df Mean Square

F p-value

All 1792.80911 162.983 1.17

3

0.332

Within

groups6250.700

45 138.904

13 to 18232.190

11 21.108 1.22

4

0.299

Within groups 775.950 45 17.243

19 to 22833.146

11 75.741 3.24

3

0.003

Within groups 1051.100 45 23.358

≥ 23218.211

11 19.837 0.71

1

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 IntervalLower Upper

MAR JAN 14.250 3.417 0.007 2.480 26.020FEB 13.250 3.417 0.016 1.480 25.020APR 9.400 3.242 0.176 -1.770 20.570MAY 11.400 3.242 0.042 0.230 22.570JUN 13.400 3.242 0.008 2.230 24.570


JUL 12.600 3.242 0.015 1.430 23.770AUG 14.400 3.242 0.003 3.230 25.570SEP 10.800 3.242 0.066 -0.370 21.970OCT 14.800 3.242 0.002 3.630 25.970NOV 7.800 3.242 0.423 -3.370 18.970DEC 14.600 3.242 0.002 3.430 25.770

Table 4 ANOVA of Mean Seasonal Event Rates by Age Group

Age Group Sum of Squares

df Mean Square

F p-value

All 791.9693 263.990 1.92

9

0.136

Within

groups7251.540

53 136.822

13 to 1852.466

3 17.489 0.97

0

0.414

Within groups 955.674 53 18.032

19 to 22378.427

3 126.142 4.44

0

0.007

Within groups 1505.818 53 28.412

≥ 23105.050

3 35.017 1.35

5

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 IntervalLower Upper

SpringSummer 6.038 1.981 0.018 0.78 11.29Fall 3.705 1.981 0.253 -1.55 8.96Winter 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 2.83Winter 0.610 2.020 0.990 -4.75 5.97

Fall Spring -3.705 1.981 0.253 -8.96 1.55Summer 2.333 1.946 0.630 -2.83 7.50Winter 2.944 2.020 0.470 -2.41 8.30

Winter Spring -6.648 2.053 0.011 -12.09 -1.20Summer -0.610 2.020 0.990 -5.97 4.75Fall -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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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

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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

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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


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

lisa barnes phc6946 internship paper

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