Abstract
Background
Gonorrhea diagnosis rates in the United States increased by 75% during 2009–2017, predominantly in men. It is unclear whether the increase among men is being driven by more screening, an increase in the prevalence of disease, or both. We sought to evaluate changes in gonorrhea testing patterns and positivity among men in Massachusetts.
Methods
The analysis included men (aged ≥15 years) who received care during 2010–2017 in 3 clinical practice groups. We calculated annual percentages of men with ≥1 gonorrhea test and men with ≥1 positive result, among men tested. Log-binomial regression models were used to examine trends in these outcomes. We adjusted for clinical and demographic characteristics that may influence the predilection to test and probability of gonorrhea disease.
Results
On average, 306 348 men had clinical encounters each year. There was a significant increase in men with ≥1 gonorrhea test from 2010 (3.1%) to 2017 (6.4%; adjusted annual risk ratio, 1.12; 95% confidence interval, 1.12–1.13). There was a significant, albeit lesser, increase in the percentage of tested men with ≥1 positive result (1.0% in 2010 to 1.5% in 2017; adjusted annual risk ratio, 1.07; 95% confidence interval, 1.04–1.09).
Conclusions
We estimated significant increases in the annual percentages of men with ≥1 gonorrhea test and men with ≥1 positive gonorrhea test result between 2010 and 2017. These results suggest that observed increases in gonorrhea rates could be explained by both increases in screening and the prevalence of gonorrhea.
Keywords: gonorrhea, electronic medical records, gonorrhea screening
We evaluated whether increased gonorrhea rates among men in Massachusetts were driven by more screening or more disease. We estimated significant increases in men with ≥1 test and significant, albeit lesser, increases in men with ≥1 positive test result.
Gonorrhea diagnosis rates in the United States increased by 75% from 2009 through 2017, predominantly in men [1]. The Massachusetts Department of Public Health also observed increases in gonorrhea diagnosis rates among men. Between 2010 and 2017, gonorrhea cases increased 329% among men in Massachusetts and in 2017, the rate among men was more than twice the rate among women [2].
There are several possible explanations for the observed increase in gonorrhea rates among men including more screening for gonorrhea, an increase in the prevalence of gonorrhea, or a combination of both. Policy changes for men who have sex with men (MSM) may have led to an increase in screening. Starting in 2010, the Centers for Disease Control and Prevention (CDC) treatment guidelines for sexually transmitted diseases (STDs) recommended annual screening for gonorrhea at all anatomic sites of sexual contact (urethra, rectum, pharynx) among sexually active MSM, regardless of condom use. The CDC also recommended screening for gonorrhea every 3–6 months in MSM who reported high-risk sexual behavior [3]. In 2014, the CDC recommended sexually transmitted infection (STI) screening for users of human immunodeficiency virus (HIV) pre-exposure prophylaxis (PrEP) every 6 months [4] and updated guidelines to recommend screening PrEP users at high risk for recurrent STIs every 3 months in 2017 [5].
The observed increase in gonorrhea rates among men could also reflect an increase in the underlying prevalence of gonorrhea. Estimates from the STD Surveillance Network indicate that the proportion of MSM tested for gonorrhea who had a positive oropharyngeal or rectal result increased by 13% and 72%, respectively, from 2009 through 2015 [6]. The prevalence of extragenital gonorrhea could be rising because of changes in sexual behavior, such as finding sexual partners online [7] or increases in condomless sex [8, 9]. Circulation of antibiotic-resistant gonorrhea could also increase disease prevalence [10].The objective of the current study was to evaluate whether observed increases in gonorrhea rates among men in Massachusetts may be due to more screening for gonorrhea, an increase in the prevalence of gonorrhea, or a combination of these factors.
METHODS
Study Population
Three clinical practice groups in Massachusetts were included in our analysis. Atrius Health serves a well-insured population of 800 000 people, primarily in eastern Massachusetts. Cambridge Health Alliance serves 300 000 individuals and is a safety net for vulnerable populations, including immigrants, within communities north of Boston. The Massachusetts League of Community Health Centers (MLCHC) serves approximately 400 000 people at federally qualified community health centers throughout the state. Data from the MLCHC were restricted to 5 community health centers with continuous gonorrhea testing data available throughout the study period. These 5 centers provide care to approximately 100 000 MLCHC patients. Overall, the practice groups we included cover a diverse population of approximately 1 million people, which represents 20% of the Massachusetts population.
Electronic medical record (EMR) data from the practice groups were used for these analyses. EMR data were accessed via the Electronic medical record Support for Public Health (ESP) system. The ESP system is an open-source public health surveillance platform that uses daily extracts of data from EMR systems to identify and report conditions of public health interest to health departments. It maps EMR data to common terms, analyzes these data for reportable diseases or updates to existing cases, and automatically submits case reports to health departments’ electronic surveillance systems (esphealth.org) [11–15].
Our analyses included men who were ≥15 years of age with ≥1 clinical encounter from 1 January 2010 through 31 December 2017 at 1 of the practice groups. We defined clinical encounters as EMR records that contained any of the following: laboratory result, prescription, recorded diagnosis code, vital sign, or immunization.
Gonorrhea testing and positivity data provided via the ESP system included annual counts of clinical encounters, gonorrhea tests, and positive gonorrhea test results for each man in our study sample. The practice groups offered nucleic acid amplification tests (NAATs) and culture tests for gonorrhea between 2010 and 2017. More than 99% of tests were NAATs during the study period, and this proportion was consistent from year to year.
Statistical Methods
The outcomes of interest are (1) trends in gonorrhea screening and (2) trends in the prevalence of gonorrhea in the entire study population. However, we cannot directly calculate these outcomes because we do not know the gonorrhea status of men who did not receive a gonorrhea test. However, if we make several statistical assumptions, including conditional independence, we can estimate these outcomes with the available data. The Supplementary Material and Young et al [16] provide details about the assumptions necessary for this approach.
To implement this approach, log-binomial regression models were used to assess trends in (1) annual percentages of men in the study population with ≥1 gonorrhea test and (2) annual percentages of men with ≥1 positive test result among men tested for gonorrhea. We created “linear models” with a single linear term for calendar year. These models assume that the risk ratio (RR) is constant for each year relative to the previous year. As a comparison, we created “flexible models,” which included indicators for each calendar year (excluding the reference year 2010). These models place no constraints on the RR for each year relative to the previous year. Our RR estimates for the flexible models represent a comparison of the risk in each given year with that in the reference year 2010.
The log-binomial regression models were adjusted for common risk factors of gonorrhea testing and disease to reduce differences in disease risk between tested and untested individuals [16]. Risk factors for testing and disease were selected a priori and included age, race/ethnicity, HIV status, PrEP use, gonorrhea symptoms, high-risk sexual behavior, and the number of gonorrhea, chlamydia, and syphilis tests and diagnoses during the past 2 years [1, 3–5, 17, 18]. Age was recorded on 31 December of each year and included as a linear function in regression models. Race/ethnicity was categorized as non-Hispanic white, non-Hispanic black, Hispanic, non-Hispanic Asian, other, and missing. The “other” race/ethnicity category included non-Hispanic American Indians, Native Americans, Alaskan Natives, and persons with >1 race recorded or race categorized as other.
Men were categorized as living with HIV if they had HIV diagnosed at 1 of the 3 practice groups by 31 December 2017. PrEP use was measured each year for each man at each practice group and was defined as ≥2 prescriptions for emtricitabine/tenofovir disoproxil fumarate ≥2 months apart while the man’s HIV status was negative. Men were considered to have gonorrhea symptoms if they had ≥1 diagnosis code for urethritis, urethral discharge, dysuria, epididymitis, testicular pain, proctitis, rectal bleeding, pharyngitis, tonsillitis, throat pain, conjunctivitis, or eye pain recorded up to 7 days before or after a gonorrhea test.
Sexual behavior associated with gonorrhea risk was defined by diagnosis codes for high-risk sexual behavior and measured each year. The complete lists of International Classification of Diseases, Ninth Revision (ICD-9) and International Classification of Diseases, Tenth Revision (ICD-10) codes for symptoms and high-risk sexual behavior are included in Supplementary Tables 1 and 2, respectively. The total number of chlamydia, syphilis, and gonorrhea tests and diagnoses during the past 2 years were calculated annually for each man with a clinical encounter in that year and were included as linear functions in regression models. Data were available on testing and diagnoses from 2008 onward.
Individual men could contribute data in multiple calendar years. Therefore, generalized estimating equation methods were used to account for within-person correlation induced by the same individuals contributing >1 record in a given regression model.
Sensitivity Analyses
We conducted 2 sensitivity analyses to assess the robustness of our findings. In the first, we examined trends in men with ≥1 symptomatic gonorrhea test from 2010 through 2017 to provide additional evidence regarding trends in gonorrhea screening. For this analysis, we defined symptomatic testing as a test with ≥1 gonorrhea symptom recorded up to 7 days before or after the test. We then calculated the proportion of men with ≥1 symptomatic gonorrhea test per year among all men tested, and the proportion of men with ≥1 positive symptomatic test per year among all men who had positive test results. We used log-binomial regression to examine linear trends in these proportions and adjusted for risk factors of gonorrhea testing and disease.
In our second sensitivity analysis we assessed trends in gonorrhea test rates and positive gonorrhea test result rates, using counts of clinical encounters and tests, respectively, as the denominators. Our primary analysis was person based and included 1 test, or 1 positive test result, per man in each year. However, men may have had multiple tests or positive test results per year, and increases in these quantities could also explain the observed increase in gonorrhea rates. To evaluate this, we used Poisson regression with generalized estimating equation methods and an offset for the number of clinical encounters or tests each man had during a calendar year. These models estimated annual rate ratios and 95% confidence intervals (CIs) for each outcome. We also compared linear and flexible models.
Data analyses were conducted using SAS software, version 9.4 (SAS Institute). The Harvard Pilgrim Health Care Institutional Review Board determined this study to be exempt from review owing to the lack of personally identifiable data.
RESULTS
A total of 678 134 men had ≥1 clinical encounter between 2010 and 2017, with an average of 306 348 men (range, 298 244 to 318 733) having ≥1 clinical encounter each year. Of these, 78 763 men had ≥1 gonorrhea test and 1184 men had ≥1 positive gonorrhea test result during the study period (Table 1). In 2010, there were 0.35 cases of gonorrhea per 1000 men with clinical encounters, compared with 1.16 cases of gonorrhea per 1000 men with clinical encounters in 2017.
Table 1.
Characteristics of Men with ≥1 Clinical Encounter, Men with ≥1 Gonorrhea Test, and Men with ≥1 Positive Gonorrhea Test, 2010–2017
| Men, No. (%) | |||
|---|---|---|---|
| Characteristic | ≥1 Clinical Encounter (n = 678 134) | ≥1 Test (n = 78 763) | ≥1 Positive Test Result (n = 1184) |
| Age, y | |||
| 15–24 | 93 081 (14) | 16 565 (21) | 201 (17) |
| 25–34 | 143 272 (21) | 29 856 (38) | 489 (41) |
| 35–44 | 121 231 (18) | 15 794 (20) | 236 (20) |
| 45–54 | 107 632 (16) | 9286 (12) | 172 (15) |
| 55–64 | 97 274 (14) | 5179 (7) | 70 (6) |
| ≥65 | 115 644 (17) | 2083 (3) | 16 (1) |
| Race/ethnicity | |||
| Non-Hispanic white | 417 030 (62) | 41 512 (53) | 490 (41) |
| Non-Hispanic black | 53 953 (8) | 13 432 (17) | 368 (31) |
| Hispanic | 12 177 (2) | 2527 (3) | 44 (4) |
| Non-Hispanic Asian | 40 389 (6) | 4755 (6) | 40 (3) |
| Non-Hispanic other | 54 845 (8) | 8578 (11) | 132 (11) |
| Unknown/missing | 99 740 (15) | 7959 (10) | 110 (9) |
| Living with HIV | 2172 (<1) | 1386 (2) | 86 (7) |
| PrEP usea | 654 (<1) | 625 (1) | 108 (9) |
| High-risk sexual behaviorb | … | 1346 (2) | 82 (7) |
Abbreviations: HIV, human immunodeficiency virus; PrEP, pre-exposure prophylaxis.
aMen prescribed HIV PrEP between 2010 and 2017.
bDiagnosis code for high-risk sexual behavior, measured up to 7 days before or after a gonorrhea test.
The percentage of men with ≥1 gonorrhea test increased from 3.1% in 2010 to 6.4% in 2017. Figure 1 depicts the results of the adjusted linear and flexible log-binomial regression models assessing trends in gonorrhea testing. The results of the linear model indicate that the proportion of men who received a gonorrhea test in any year was 1.12 (95% CI, 1.12–1.13) times the proportion in the previous year. Therefore, in 2017, the proportion of men who received a gonorrhea test was 2.25 (95% CI, 2.20–2.31) times the proportion in 2010. Visual comparison of the RR estimates from the linear and flexible models qualitatively indicates that the linear assumption was reasonable, given the similarity of the estimates from both models.
Figure 1.
Estimated associations between calendar year and gonorrhea testing among men with clinical encounters, 2010–2017. Black circles represent risk ratio (RR) point estimates from the flexible model, which included an indicator for each calendar year. Error bars above and below the circles represent the 95% confidence intervals (CIs) from the model. The solid gray line represents the linear trend in the RR point estimates from the linear model; the dotted lines, 95% CIs for the linear trend line; and the line at 1, the null effect for a ratio measure. The RRs were adjusted for age, race/ethnicity, human immunodeficiency virus status, pre-exposure prophylaxis use, and the numbers of gonorrhea, chlamydia, and syphilis tests and diagnoses during the past 2 years.
In 2010, 1.0% of men tested for gonorrhea had a positive result, and in 2017, 1.5% of men tested for gonorrhea had a positive result. The results from our second set of adjusted models are depicted in Figure 2. The linear model indicates that the proportion of men who had a positive gonorrhea test result in each year was 1.07 (95% CI, 1.04–1.09) times the proportion in the previous year. In this case, visual comparison of the RR estimates from the linear and flexible models suggests that, even though both models convey an increasing trend, the linear assumption is overly restrictive. The results from the flexible model indicate that the proportion of men who had a positive gonorrhea test result in 2011 was 0.69 (95% CI, .51–.92) times the proportion in 2010, and the proportion who had a positive gonorrhea test result in 2017 was 1.53 (1.21–1.91) times the proportion in 2010.
Figure 2.
Estimated associations between calendar year and positive gonorrhea tests among men with ≥1 test, 2010–2017. Black circles represent risk ratio (RR) point estimates from the flexible model, which included an indicator for each calendar year. Error bars above and below each black circle represent the 95% confidence intervals (CIs) from the model. The solid gray line represents the linear trend in RR point estimates from the linear model; the dotted lines, 95% CIs for the linear trend line; and the line at 1, the null effect for a ratio measure. The RRs were adjusted for age, race/ethnicity, human immunodeficiency virus status, pre-exposure prophylaxis use, gonorrhea symptoms, high-risk sexual behavior, and the numbers of gonorrhea, chlamydia, and syphilis tests and diagnoses during the past 2 years.
Adjusted trends in men with ≥1 symptomatic test from our sensitivity analysis are depicted in Figure 3. The proportion of men tested for gonorrhea who had a symptomatic test decreased from 20% in 2010 to 16% in 2017 (RR, 0.96; 95% CI, .95–.96). The proportion of men with positive test results who had a symptomatic test remained relatively constant over time; in both 2010 and 2017, 65% of men with a positive test result had symptoms (RR, 1.01; 95% CI, .99–1.03).
Figure 3.
Percentages of men with ≥1 symptomatic gonorrhea test and ≥1 positive symptomatic gonorrhea test result, 2010–2017. Gonorrhea symptoms included urethritis, urethral discharge, dysuria, epididymitis, testicular pain, proctitis, rectal bleeding, pharyngitis, tonsillitis, throat pain, conjunctivitis, and eye pain, which were documented in the medical record up to 7 days before or after a gonorrhea test. Dashed lines represent linear trends in men with ≥1 symptomatic test and ≥1 positive symptomatic test result between 2010 and 2017. The linear trends were adjusted for age, race/ethnicity, human immunodeficiency virus status, pre-exposure prophylaxis use, high-risk sexual behavior, and the numbers of gonorrhea, chlamydia, and syphilis tests and diagnoses during the past 2 years.
The results of our test-based analyses were very similar to our primary, or person-based, analyses (Supplementary Figures 1–2). The adjusted annual rate ratio for gonorrhea testing was 1.16 (95% CI, 1.16–1.17) from the linear model. The adjusted annual rate ratio for positive gonorrhea tests was 1.05 (95% CI, 1.02–1.08) from the linear model. However, the flexible model for positive gonorrhea test results indicates that the rate of positive test results in 2011 was 0.65 (95% CI, .49–.88) times the rate in 2010, and the rate in 2017 was 1.39 (1.10–1.76) times that in 2010.
DISCUSSION
Among men receiving care at 3 clinical practice groups in Massachusetts, we found significant increases in the proportion of men with ≥1 gonorrhea test and the proportion of tested men with ≥1 positive result between 2010 and 2017. Our results were consistent when we examined trends in gonorrhea testing rates and positivity rates. These findings suggest that the observed increases in gonorrhea can be explained by both increases in testing and the prevalence of gonorrhea during this period.
Between 2010 and 2017, the proportion of men who were tested for gonorrhea in a given year more than doubled in our study population. In 2010, the CDC released revised STD treatment guidelines that recommended screening MSM annually for gonorrhea, and screening MSM who report high-risk sexual behavior for STDs every 3–6 months [3]. The Massachusetts Department of Public Health also released a clinical advisory about management of gonorrhea to providers in 2015 [19]. These publications may have heightened awareness about gonorrhea and prompted healthcare providers to screen more men. In addition, the period we evaluated coincided with the US Food and Drug Administration’s approval of PrEP [20] and the release of CDC recommendations to regularly screen PrEP users for STIs [4, 5]. Our finding that the proportion of men who had ≥1 gonorrhea symptom at the time of their test decreased also indicates that diagnostic testing for gonorrhea may have decreased. Thus, the observed increases in testing could be due, in part, to increased screening for gonorrhea.
We also estimated a 50% increase in the proportion of men with ≥1 positive test result for gonorrhea among those tested between 2010 and 2017. The prevalence of gonorrhea may be increasing because of changes in sexual behavior among men, particularly among MSM. Annual cross-sectional surveys of gay and bisexual men in Australia found that self-reported consistent condom use decreased among respondents from 46% in 2013 to 31% in 2017, while condomless anal intercourse increased from 29% in 2013 to 43% in 2017 [9]. MSM who initiated PrEP between 2014 and 2017 at an STD clinic in Seattle, Washington, were also more likely to report never using condoms 1 year after initiating PrEP [21]. Men also report meeting sexual partners online more frequently during this time period [7, 22]. In a 2017 study by Paz-Bailey et al [22] the percentage of MSM reporting frequent internet usage to find sexual partners increased from 21% in 2008 to 44% in 2014, and MSM who reported using the internet frequently to find sexual partners were more likely than those who did not find partners online to report having more sexual partners during the past 12 months. Notably, improvements in gonorrhea surveillance and a shift from culture-based testing to NAATs could explain some of the observed increases in gonorrhea nationally. However, in our current study sample, >99% of tests were NAATs, and this was consistent through 2017.
Our analysis has several limitations. We assumed conditional independence, that is, the probability of receiving a gonorrhea test was independent of gonorrhea disease, conditional on the covariates in our regression models [16]. This is an untestable assumption, and differences in gonorrhea risk between tested and untested men may remain. We were unable to ascertain the sex of sexual partners for men in our study. However, we incorporated variables that may be proxy markers of MSM, such as PrEP use, high-risk sexual behavior, and counts of previous STD tests and diagnoses. Despite recommendations regarding extragenital gonorrhea testing, we were unable to ascertain the anatomic site of each test. Misclassification of HIV/STI status and PrEP use could also occur if patients received care at other clinics not included in our study. Gonorrhea testing and positivity in the 3 practice groups in our study may not reflect gonorrhea testing and positivity across the state. However, the practice groups we selected included private and publicly funded sites, and findings of a previous study suggest that they are representative of the state population [13]. In addition, men were categorized as having gonorrhea symptoms or practicing high-risk sexual behavior if they had an appropriate ICD-9 or ICD-10 code recorded in their health record. Free-text symptom and behavioral information recorded in medical notes was not captured in our analyses, and we may have misclassified gonorrhea symptoms and high-risk sexual behavior.
Despite these limitations, the data and the analyses presented here provide unique insights into gonorrhea testing and positivity among men in Massachusetts. Public health agencies in the United States typically only receive reports of positive gonorrhea tests results for disease surveillance purposes. These data allow them to calculate gonorrhea diagnosis rates in their jurisdiction. However, they do not receive reports of patients with negative or indeterminate results, nor do they receive data on the number of patients who have medical encounters in a given period. Thus, public health agencies typically lack data necessary to calculate disease testing and positivity as presented here. In addition, adjustment for a rich set of common risk factors for testing and disease allowed us to use a novel approach to estimate trends in gonorrhea screening and prevalence of disease [16].
In conclusion, we estimated a doubling in the proportion of men who received ≥1 gonorrhea test in a year between 2010 and 2017, and a significant, albeit lesser, increase in the proportion of men with ≥1 positive result. These results suggest that the rise in gonorrhea rates among men in Massachusetts could be driven by both more screening and an increase in the prevalence of gonorrhea.
Supplementary Data
Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
Notes
Acknowledgments. The authors would like to thank Atrius Health, Cambridge Health Alliance, the Massachusetts League of Community Health Centers, and Commonwealth Informatics for data collection activities.
Disclaimer. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
Financial support. This work was supported by the Division of STD Prevention, Centers for Disease Control and Prevention, US Department of Health and Human Services, through the STD Surveillance Network Part B (grant CDC-RFA-PS13-1306) and by the Massachusetts Department of Public Health, Commonwealth of Massachusetts.
Potential conflicts of interest. J. Y. reports contracts through her department from the Centers for Disease Control and Prevention and the Massachusetts Department of Public Health and grants from the National Institutes of Health, outside the submitted work. J. L. M. reports receiving personal fees for consulting on a research grant to Kaiser Permanente Northern California from Gilead Sciences, outside the submitted work. K. E. reports receiving personal fees from Massachusetts Department of Public Health, outside the submitted work. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
References
- 1. Centers for Disease Control and Prevention. Sexually transmitted disease surveillance 2017. Atlanta, GA: US Department of Health and Human Services, 2018. [Google Scholar]
- 2. Massachusetts Department of Public Health, Bureau of Infectious Disease and Laboratory Sciences. Overview of sexually transmitted disease surveillance data, Massachusetts, 1990–2017 Available at: https://www.mass.gov/lists/std-data-and-reports#data-from-massachusetts. Accessed 7 May 2019.
- 3. Centers for Disease Control and Prevention. Sexually transmitted diseases treatment guidelines, 2010. MMWR 2010; 59:1–110. [Google Scholar]
- 4. Centers for Disease Control and Prevention. US Public Health Service: Preexposure prophylaxis for the prevention of HIV infection in the United States—2014 clinical practice guideline Available at: https://www.cdc.gov/hiv/pdf/guidelines/PrEPguidelines2014.pdf. Accessed 5 October 2018.
- 5. Centers for Disease Control and Prevention. US Public Health Service: Preexposure prophylaxis for the prevention of HIV infection in the United States—2017 update: a clinical practice guideline Available at: https://www.cdc.gov/hiv/pdf/risk/prep/cdc-hiv-prep-guidelines-2017.pdf. Accessed 14 September 2018.
- 6. Weston EJ, Kirkcaldy RD, Stenger M, Llata E, Hoots B, Torrone EA. Narrative review: assessment of Neisseria gonorrhoeae infections among men who have sex with men in national and sentinel surveillance systems in the United States. Sex Transm Dis 2018; 45:243–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Cabecinha M, Mercer CH, Gravningen K, et al. Finding sexual partners online: prevalence and associations with sexual behaviour, STI diagnoses and other sexual health outcomes in the British population. Sex Transm Infect 2017; 93:572–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Grace D, Jollimore J, MacPherson P, Strang MJP, Tan DHS. The pre-exposure prophylaxis-stigma paradox: learning from Canada’s first wave of PrEP users. AIDS Patient Care STDS 2018; 32:24–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Holt M, Lea T, Mao L, et al. Community-level changes in condom use and uptake of HIV pre-exposure prophylaxis by gay and bisexual men in Melbourne and Sydney, Australia: results of repeated behavioural surveillance in 2013-17. Lancet HIV 2018; 5:e448–56. [DOI] [PubMed] [Google Scholar]
- 10. Chesson HW, Kirkcaldy RD, Gift TL, Owusu-Edusei K Jr, Weinstock HS. Ciprofloxacin resistance and gonorrhea incidence rates in 17 cities, United States, 1991–2006. Emerg Infect Dis 2014; 20:612–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Centers for Disease Control and Prevention. Automated detection and reporting of notifiable diseases using electronic medical records versus passive surveillance—Massachusetts, June 2006--July 2007. MMWR 2008; 57:373–6. [PubMed] [Google Scholar]
- 12. Klompas M, McVetta J, Lazarus R, et al. Integrating clinical practice and public health surveillance using electronic medical record systems. Am J Public Health 2012; 102(suppl 3):S325–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Klompas M, Cocoros NM, Menchaca JT, et al. State and local chronic disease surveillance using electronic health record systems. Am J Public Health 2017; 107:1406–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Lazarus R, Klompas M, Campion FX, et al. Electronic Support for Public Health: validated case finding and reporting for notifiable diseases using electronic medical data. J Am Med Inform Assoc 2009; 16:18–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Vogel J, Brown JS, Land T, Platt R, Klompas M. MDPHnet: secure, distributed sharing of electronic health record data for public health surveillance, evaluation, and planning. Am J Public Health 2014; 104:2265–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Young JG, Willis SJ, Klompas M, Marcus JL. More testing or more disease? a counterfactual approach to explaining observed increases in positive tests over time arXiv:1904.07208. Available at: https://arxiv.org/pdf/1904.07208.pdf. Accessed 15 April 2019.
- 17. Leichliter JS, Ellen JM, Gunn RA. STD repeaters: implications for the individual and STD transmission in a population. In: Aral SO, Douglas JM, eds. Behavioral interventions for prevention and control of sexually transmitted diseases. Boston, MA: Springer, 2007:354–73. [Google Scholar]
- 18. Hsu KK, Molotnikov LE, Roosevelt KA, et al. Characteristics of cases with repeated sexually transmitted infections, Massachusetts, 2014–2016. Clin Infect Dis 2018; 67:99–104. [DOI] [PubMed] [Google Scholar]
- 19. Coughlin B, Hsu K. Massachusetts Department of Public Health Bureau of Infectious Disease clinical advisory: update to recommended treatment and management of gonococcal infections, June 24, 2015 Available at: https://mass.gov/doc/update-to-recommended-treatment-and-management-of-gonococcal-infections-june-24-2015/download Accessed 3 February 2020.
- 20. US Food and Drug Administration. Truvada for PrEP fact sheet: ensuring safe and proper use Available at: https://www.fda.gov/downloads/Drugs/DrugSafety/PostmarketDrugSafetyInformationforPatientsandProviders/UCM312290.pdf. Accessed 15 October 2018.
- 21. Montaño MA, Dombrowski JC, Dasgupta S, et al. Changes in sexual behavior and STI diagnoses among MSM initiating PrEP in a clinic setting. AIDS Behav 2019; 23:548–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Paz-Bailey G, Hoots BE, Xia M, Finlayson T, Prejean J, Purcell DW; NHBS Study Group Trends in internet use among men who have sex with men in the United States. J Acquir Immune Defic Syndr 2017; 75(suppl 3):288–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.