Gonorrhea is the second most commonly reported identifiable disease in the United States (U.S.). Importantly, more than 25% of gonorrheal infections demonstrate antibiotic resistance, leading the Centers for Disease Control and Prevention (CDC) to classify gonorrhea as an “urgent threat”.
We examined the association of gonorrhea infection rates with the incidence of HIV and socioeconomic factors. A county-level multivariable model was then constructed.
Multivariable analysis demonstrated that HIV incidence [Coefficient (Coeff): 1.26, 95% Confidence Interval (CI): 0.86, 1.66, P<0.001] exhibited the most powerful independent association with the incidence of gonorrhea and predicted 40% of the observed variation in gonorrhea infection rates. Sociodemographic factors like county urban ranking (Coeff: 0.12, 95% CI: 0.03, 0.20, P = 0.005), percentage of women (Coeff: 0.41, 95% CI: 0.28, 0.53, P<0.001) and percentage of individuals under the poverty line (Coeff: 0.45, 95% CI: 0.32, 0.57, P<0.001) exerted a secondary impact. A regression model that incorporated these variables predicted 56% of the observed variation in gonorrhea incidence (Pmodel<0.001, R2 model = 0.56).
Gonorrhea and HIV infection exhibited a powerful correlation thus emphasizing the benefits of comprehensive screening for sexually transmitted infections (STIs) and the value of pre-exposure prophylaxis for HIV among patients visiting an STI clinic. Furthermore, sociodemographic factors also impacted gonorrhea incidence, thus suggesting another possible focus for public health initiatives.
Citation: Andreatos N, Grigoras C, Shehadeh F, Pliakos EE, Stoukides G, Port J, et al. (2017) The impact of HIV infection and socioeconomic factors on the incidence of gonorrhea: A county-level, US-wide analysis. PLoS ONE 12(9): e0183938. https://doi.org/10.1371/journal.pone.0183938
Editor: William M. Shafer, Emory University School of Medicine, UNITED STATES
Received: June 16, 2017; Accepted: August 14, 2017; Published: September 1, 2017
Copyright: © 2017 Andreatos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All utilized data were drawn from publicly available datasets, namely the National Center for HIV/AIDS, Viral Hepatitis, Sexually Transmitted Diseases, and Tuberculosis Prevention (NCHHSTP) Atlas database (https://gis.cdc.gov/grasp/nchhstpatlas/main.html?value=atlas.) and the US Census Bureau (https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml.), as described in the Methods section of the manuscript.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
According to the Centers for Disease Control and Prevention (CDC), gonorrhea is the second most commonly reported identifiable disease in the United States (U.S.) [1, 2]. At the same time, the CDC estimates that more than 25% of new Neisseria gonorrhoeae infections demonstrate antibiotic resistance and has classified gonorrhea as an “urgent threat” . Importantly, inadequately treated infections often result in serious complications and long-term morbidity, while infected individuals may remain asymptomatic for extensive periods thus facilitating disease transmission . Moreover, gonococcal infections promote the transmission of HIV thus exerting a disproportionately severe, indirect impact on the reproductive health of the community [5, 6].
Given the scarcity of available public health resources, effective interventions must be targeted at high-risk populations. Such a strategy would be particularly effective against gonorrhea, as wide variations in disease incidence exist in different sociodemographic groups [7, 8]. Even though several studies have examined the influence of sociodemographic variables on the incidence and outcome of gonorrhea [4, 7, 9–13], little information exists on the precise impact and relative importance of these factors and the association of gonorrhea infection rates with the incidence of HIV on a national scale. In this cross-sectional study, we utilized nationwide data from the National Center for HIV/AIDS, Viral Hepatitis, Sexually Transmitted Diseases, and Tuberculosis Prevention (NCHHSTP) Atlas database  and the U.S. Census Bureau , in order to examine the association of gonorrhea with socioeconomic variables and HIV incidence. We then constructed a multivariable model of gonorrhea incidence on the basis of the identified variables.
Gonorrhea and HIV infection incidence rates
To estimate the gonorrhea and HIV infection rates, we extracted all relevant information from the NCHHSTP Atlas database of the CDC . Annual incidence rates for the entire U.S. population were calculated for the 2010–2014 period, the 5 most recent years with available data. We also estimated the corresponding average gonorrhea and HIV incidence rates [and 95% Poisson confidence intervals (CIs)] over the 2010–2014 time-period, for each state and county with available data. The District of Columbia and Puerto Rico were each considered as a state for the purpose of our analysis and all average incidence rates were adjusted for county population, before being incorporated into the county-level analysis.
Socioeconomic information for each county was obtained from the 2010–2014 American Community Survey (ACS) 5-year estimates dataset . This dataset and the aforementioned time-horizon were selected due to their proven reliability in estimating demographic variables from U.S. counties . Information on the following independent variables was extracted and included in our analysis: a) Percentage of individuals living under the poverty line, b) Percentage of individuals of Black, Hispanic or White race/ethnicity, c) Percentage of women, and, d) Urban vs. rural ranking of counties.
We performed univariable linear regression to examine the association of population-adjusted, average gonorrhea incidence rates at the county level with possible predictive variables, specifically: population-adjusted, average HIV diagnosis rate, percentage of individuals living under the poverty line, population distribution according to race and gender and county urban vs. rural ranking. A multivariable linear regression model was then constructed by incorporating variables that were significantly associated with gonorrhea incidence in univariable analysis and were judged to be important from a biomedical perspective.
The regression coefficient of each variable (Coeff), the corresponding P value and the coefficient of determination (R2), indicating the proportion of variance in the dependent variable that stemmed from the independent variable, were calculated in each case. To facilitate comparisons, the statistical distribution of each variable was appropriately adjusted, so as to produce a mean of 0 and a standard deviation (SD) of 1 (Z-score). We employed Belsley’s test to examine collinearity between independent variables . Statistical significance was defined as P<0.05. All data processing and statistical analyses were performed using STATA v.14 software (StataCorp LP, College Station, TX). Incidence rate choropleth maps were created using the Quantum Geographic Information System (QGIS)  and a hotspot analysis map was generated with the aid of the ArcGIS online platform (Environmental Systems Research Institute, Redlands CA) .
Results and discussion
A total of 1,638,863 N. gonorrhoeae infections were reported in 3,220 counties or county-equivalents (from the U.S., Puerto Rico, the U.S. Virgin Islands and Guam) between 2010 and 2014. The adjusted average gonorrhea incidence rate was 103.30 cases per 100,000 people (95% CI: 103.14, 103.45) and the incidence of gonorrhea increased from 100.19 cases per 100,000 people in 2010 to 109.79 cases per 100,000 people in 2014. The District of Columbia [303.26 cases per 100,000 people (95% CI: 297.21, 309.40)], Mississippi [197.17 cases per 100,000 people (95% CI: 194.92, 199.43)] and Louisiana [194.10 cases per 100,000 people (95% CI: 192.31, 195.91)] had the highest reported rates, while New Hampshire [11.74 cases per 100,000 people (95% CI: 10.92, 12.59)], Wyoming [10.86 cases per 100,000 people (95% CI: 9.69, 12.13)] and Puerto Rico [9.90 cases per 100,000 people (95% CI: 9.45, 10.37)] had the lowest rates of N. gonorrhoeae infection. Overall, the highest incidence rates were concentrated in the Southern states (Figs 1 and 2, Table 1).
Choropleth Map of Gonorrhea Incidence in the United States, 2010–2014.
Gonorrhea Incidence Hotspot Analysis in the United States, 2010–2014.
Repeated simple linear regression was performed to examine the association of a number of independent variables with gonorrhea incidence rates. Average HIV incidence rates, percentage of women, percentage of individuals of Black race, percentage of individuals under the poverty line and county urban ranking were positively associated with the incidence of gonorrhea (Table 2, all variables were population-adjusted).
Subsequently, the independent variables identified through the univariable analysis were combined to produce a multivariable linear regression model. HIV incidence rates, county urban ranking, percentage of women and percentage of individuals under the poverty line (Table 2) were independent predictors of gonorrhea incidence (Pmodel<0.001, R2 model = 0.56).
Notably, although Black race has previously been associated with increased risk of gonorrheal infection [8, 10, 11, 13, 20], we elected not to include race in the final model. This decision stemmed from the fact that race primarily reflects disparities in access to healthcare and the overall standard of living, as evidenced by the collinearity that race displayed with other demographic variables. In turn, this finding suggests that race could often simply function as a proxy of socioeconomic conditions, rather than a true independent risk factor and should be used with caution in epidemiologic studies [21, 22]. A patient-level analysis may be better suited to identifying the relative impact of the socioeconomic and biologic risk factors (such as absence of the CCR5 mutation in individuals of black race, leading to increased susceptibility to HIV [23–25]) that might underlie the association of race with the incidence of STIs.
Importantly, HIV incidence rates had a powerful association with N. gonorrhoeae infection. Sexually transmitted infections not only share common risk factors with HIV infection, but also facilitate HIV transmission [5, 26] and previous studies have reported that N. gonorrhoeae infection may result in up to a 3-fold increase in the risk of HIV seroconversion [6, 27, 28]. This is noteworthy, given the morbidity and healthcare costs associated with HIV infection. For example, a recent study estimated the lifetime cost of HIV infection at approximately $326,500  and, as these figures were based on conservative assumptions, overall costs of HIV infection may be even higher in practice. Although previous trials that assessed the impact of treatment for STIs on HIV incidence produced inconclusive results [30, 31], some evidence suggests that timely therapy for STIs, including gonorrhea, may help to decrease HIV transmission, particularly in the setting of high STI and HIV incidence [32, 33].
Moreover, our findings emphasize the importance of preventive measures whenever an STI, such as gonorrhea, is diagnosed. For example, given the proven efficacy and relative safety of pre-exposure prophylaxis (PrEP) for HIV, health care providers should consider whether PrEP should be offered to patients with a history of an STI [34–36]. Recent recommendations of the International Antiviral Society-USA Panel suggest that PrEP should be discussed with patients that were recently diagnosed with an STI, particularly if their yearly risk of contracting HIV is 2% or more [34, 36]. Interestingly, a recent study by Solomon et al. suggested that Men who have Sex with Men and are diagnosed with syphilis are at very high risk for HIV seroconversion and may constitute ideal candidates for PrEP . Although it may be tempting to hypothesize that an association may also exist between N. gonorrhoeae infection and subsequent HIV seroconversion, the present population-based study cannot establish causation or determine the nature and direction of the association between gonorrhea and HIV infection on an individual level. Patient-level analyses are needed to investigate the interplay of HIV and N. gonorrhoeae infection and determine the precise indications of PrEP administration in this patient group. Moreover, it must be noted that the decision to initiate PrEP does not obviate the need for patient education and appropriate counseling on sexual behavior, especially given the minimal cost and risk-free nature of such interventions .
This multivariable analysis also demonstrated that county poverty levels were independently associated with gonorrhea incidence rates. Socioeconomic disadvantage has been shown to exert an impact on N. gonorrhoeae infection [8, 11], and our results lend further credence to previous studies, by utilizing data on a national scale. Although the present analysis cannot conclusively demonstrate whether the reported association is directly causal, income status may serve as a proxy of community sexual health. Poverty is inextricably associated with low educational attainment , decreased access to health care , substance abuse [41, 42] and increased prevalence of prostitution , all of which are known to either promote high-risk sexual behavior or prevent the early identification and treatment of N. gonorrhoeae infection [10, 13]. Although some of these factors may appear challenging to address, positive changes in socioeconomic conditions may substantially impact gonorrhea incidence rates .
Lastly, our findings also point to increased gonorrhea incidence in urban counties, as well as in counties with a greater percentage of women. The first finding is consistent with the existing literature, which suggests that gonorrhea tends to be more frequent in the urban setting, particularly in areas characterized by increased population density and considerable socioeconomic disadvantage [10, 44]. Nevertheless, we must note that gonorrhea infection rates may be consistently underreported in rural areas . As such, the importance of public health initiatives in the rural setting could be underestimated. Regarding the higher overall incidence of gonorrhea in counties with a larger female population, this finding may partly be attributed to the fact that women commonly develop asymptomatic infection . In turn, although the rate of asymptomatic infection is not adequately captured by incidence statistics, asymptomatic individuals are central to disease transmission as they may unwittingly serve as a disease “reservoir”. In fact, mathematical models suggest that the presence of a preexisting high-incidence “reservoir” of N. gonorrhea infection in a sexual network may have a greater impact on disease spread than individual sexual behavior . As such, our findings underlie the importance of targeted screening programs in high-risk women, as outlined in the recommendations recently issued by the U.S. Preventive Services Taskforce .
Regarding limitations that should be considered before interpreting our findings, the present analysis relied on aggregate incidence rates and sociodemographic data, collected at the county and state level, rather than data from individual cases. As such, detailed patient-level stratification according to possible confounders was impossible to perform. Furthermore, the cross-sectional design of the study precluded the determination of cause-and-effect relationships, but consideration of the existing literature facilitates interpretation of our findings. The databases we utilized enabled us to perform a nationwide analysis and limited the possibility of sampling bias. However, the timeframe of our analysis, as well as the availability of pertinent sociodemographic variables, were limited. Moreover, the possible existence of a systematic pattern of underreporting among counties with specific sociodemographic characteristics may have introduced bias into our analysis.
In conclusion, the incidence of gonorrhea in the U.S. increased over the 2010–2014 time period. Given the high frequency of asymptomatic infection , the increasing rate of antibiotic resistance among N. gonorrhoeae strains  and the association of gonorrhea with HIV transmission , a comprehensive public health response is necessary to contain disease spread. We developed a county-level, multivariable linear regression model of N. gonorrhoeae infection, after identifying independent predictors of gonorrhea incidence on a national scale. Importantly, our analysis demonstrated that gonorrhea and HIV infection are closely associated at the population level. Although the present study cannot conclusively prove whether this association is also valid at the patient level, it suggests the potential benefits from comprehensive screening for STIs and HIV. This is particularly important, given the large number of unreported/undiagnosed STI cases [3, 47] and the low compliance with screening recommendations that is reported in the literature [48–51]. Furthermore, our findings highlight the potential value of offering pre-exposure prophylaxis for HIV to high-risk patients visiting an STI clinic, particularly in high-incidence areas. Sociodemographic factors were also associated with gonorrhea incidence, thus suggesting another possible focus for public health initiatives. Taken together, a combination of targeted screening programs, counseling on sexual behavior and comprehensive clinical management of high-risk patients with STIs may prove instrumental in curtailing the spread of gonorrhea and HIV infection. Future studies that focus on the most heavily affected areas will provide additional guidance on how to allocate scarce public health funds and design cost-effective initiatives.
- 1. Centers for Disease Control and Prevention (CDC). 2015 Sexually transmitted diseases surveillance [cited 2017 April 10]. https://www.cdc.gov/std/stats15/gonorrhea.htm.
- 2. Kirkcaldy RD, Harvey A, Papp JR, Del Rio C, Soge OO, Holmes KK, et al. Neisseria gonorrhoeae Antimicrobial Susceptibility Surveillance—The Gonococcal Isolate Surveillance Project, 27 Sites, United States, 2014. Morbidity and mortality weekly report Surveillance summaries. 2016;65(7):1–19. pmid:27414503
- 3. Centers for Disease Control and Prevention (CDC). Antimicrobial Resistance Threat Levels [cited 2017 January 15]. https://www.cdc.gov/drugresistance/biggest_threats.html.
- 4. Walker CK, Sweet RL. Gonorrhea infection in women: prevalence, effects, screening, and management. Int J Womens Health. 2011;3:197–206. pmid:21845064
- 5. Fleming DT, Wasserheit JN. From epidemiological synergy to public health policy and practice: the contribution of other sexually transmitted diseases to sexual transmission of HIV infection. Sexually transmitted infections. 1999;75(1):3–17. pmid:10448335
- 6. Hanson J, Posner S, Hassig S, Rice J, Farley TA. Assessment of sexually transmitted diseases as risk factors for HIV seroconversion in a New Orleans sexually transmitted disease clinic, 1990–1998. Ann Epidemiol. 2005;15(1):13–20. pmid:15571989
- 7. Cattley C M P, Genco C. Incidence of Gonorrhea and Chlamydia in Urban Settings: The Case for Neighborhood Level Analysis in Boston. Advances in Infectious Disease. 2015;5:162–6.
- 8. Springer YP, Samuel MC, Bolan G. Socioeconomic gradients in sexually transmitted diseases: a geographic information system-based analysis of poverty, race/ethnicity, and gonorrhea rates in California, 2004–2006. American journal of public health. 2010;100(6):1060–7. pmid:20395580
- 9. Gullette DL, Rooker JL, Kennedy RL. Factors associated with sexually transmitted infections in men and women. J Community Health Nurs. 2009;26(3):121–30. pmid:19662560
- 10. Farley TA. Sexually transmitted diseases in the Southeastern United States: location, race, and social context. Sexually transmitted diseases. 2006;33(7 Suppl):S58–64. pmid:16432486
- 11. Du P, McNutt LA, O'Campo P, Coles FB. Changes in community socioeconomic status and racial distribution associated with gonorrhea rates: an analysis at the community level. Sexually transmitted diseases. 2009;36(7):430–8. pmid:19556936
- 12. Thomas JC, Schoenbach VJ, Weiner DH, Parker EA, Earp JA. Rural gonorrhea in the southeastern United States: a neglected epidemic? American journal of epidemiology. 1996;143(3):269–77. pmid:8561161
- 13. Rice RJ, Roberts PL, Handsfield HH, Holmes KK. Sociodemographic distribution of gonorrhea incidence: implications for prevention and behavioral research. American journal of public health. 1991;81(10):1252–8. pmid:1928521
- 14. Centers for Disease Control and Prevention (CDC). NCHHSTP Atlas [cited 2017 January 15]. https://gis.cdc.gov/grasp/nchhstpatlas/main.html?value=atlas.
- 15. American Fact Finder. [cited 2016 July 15]. https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml.
- 16. US Census Bureau. When to use 1-year, 3-year, or 5-year estimates [cited 2016 September 6]. https://www.census.gov/programs-surveys/acs/guidance/estimates.html.
- 17. Belsley DA K E, Welsch RE. Detecting and Assessing Collinearity. Regression Diagnostics: John Wiley & Sons, Inc.; 2005. p. 85–191.
- 18. Open Source Geospatial Foundation Project. [cited 2016 November 5]. http://www.qgis.org/en/site/.
- 19. Environmental Systems Research Institute (ESRI). ArcGIS [cited 2017 May 10]. http://www.esri.com/arcgis/about-arcgis.
- 20. Thomas JC, Gaffield ME. Social structure, race, and gonorrhea rates in the southeastern United States. Ethn Dis. 2003;13(3):362–8. pmid:12894961
- 21. Krieger N. Refiguring "race": epidemiology, racialized biology, and biological expressions of race relations. Int J Health Serv. 2000;30(1):211–6. pmid:10707306
- 22. See I, Wesson P, Gualandi N, Dumyati G, Harrison LH, Lesher L, et al. Socioeconomic Factors Explain Racial Disparities in Invasive Community-Associated Methicillin-Resistant Staphylococcus aureus Disease Rates. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2017;64(5):597–604. pmid:28362911
- 23. Dean M, Carrington M, Winkler C, Huttley GA, Smith MW, Allikmets R, et al. Genetic restriction of HIV-1 infection and progression to AIDS by a deletion allele of the CKR5 structural gene. Hemophilia Growth and Development Study, Multicenter AIDS Cohort Study, Multicenter Hemophilia Cohort Study, San Francisco City Cohort, ALIVE Study. Science. 1996;273(5283):1856–62. pmid:8791590
- 24. Liu R, Paxton WA, Choe S, Ceradini D, Martin SR, Horuk R, et al. Homozygous defect in HIV-1 coreceptor accounts for resistance of some multiply-exposed individuals to HIV-1 infection. Cell. 1996;86(3):367–77. pmid:8756719
- 25. Samson M, Libert F, Doranz BJ, Rucker J, Liesnard C, Farber CM, et al. Resistance to HIV-1 infection in caucasian individuals bearing mutant alleles of the CCR-5 chemokine receptor gene. Nature. 1996;382(6593):722–5. pmid:8751444
- 26. Wasserheit JN. Epidemiological synergy. Interrelationships between human immunodeficiency virus infection and other sexually transmitted diseases. Sexually transmitted diseases. 1992;19(2):61–77. pmid:1595015
- 27. Laga M, Alary M, Nzila N, Manoka AT, Tuliza M, Behets F, et al. Condom promotion, sexually transmitted diseases treatment, and declining incidence of HIV-1 infection in female Zairian sex workers. Lancet. 1994;344(8917):246–8. pmid:7913164
- 28. Plummer FA, Simonsen JN, Cameron DW, Ndinya-Achola JO, Kreiss JK, Gakinya MN, et al. Cofactors in male-female sexual transmission of human immunodeficiency virus type 1. The Journal of infectious diseases. 1991;163(2):233–9. pmid:1988508
- 29. Schackman BR, Fleishman JA, Su AE, Berkowitz BK, Moore RD, Walensky RP, et al. The lifetime medical cost savings from preventing HIV in the United States. Medical care. 2015;53(4):293–301. pmid:25710311
- 30. Ng BE, Butler LM, Horvath T, Rutherford GW. Population-based biomedical sexually transmitted infection control interventions for reducing HIV infection. The Cochrane database of systematic reviews. 2011; (3):CD001220. pmid:21412869
- 31. Hayes R, Watson-Jones D, Celum C, van de Wijgert J, Wasserheit J. Treatment of sexually transmitted infections for HIV prevention: end of the road or new beginning? Aids. 2010;24 Suppl 4:S15–26. pmid:21042049
- 32. Grosskurth H, Mosha F, Todd J, Mwijarubi E, Klokke A, Senkoro K, et al. Impact of improved treatment of sexually transmitted diseases on HIV infection in rural Tanzania: randomised controlled trial. Lancet. 1995;346(8974):530–6. pmid:7658778
- 33. Stillwaggon E, Sawers L. Rush to judgment: the STI-treatment trials and HIV in sub-Saharan Africa. Journal of the International AIDS Society. 2015;18:19844. pmid:25990095
- 34. Marrazzo JM, del Rio C, Holtgrave DR, Cohen MS, Kalichman SC, Mayer KH, et al. HIV prevention in clinical care settings: 2014 recommendations of the International Antiviral Society-USA Panel. Jama. 2014;312(4):390–409. pmid:25038358
- 35. Fonner VA, Dalglish SL, Kennedy CE, Baggaley R, O'Reilly KR, Koechlin FM, et al. Effectiveness and safety of oral HIV preexposure prophylaxis for all populations. Aids. 2016;30(12):1973–83. pmid:27149090
- 36. Gunthard HF, Saag MS, Benson CA, del Rio C, Eron JJ, Gallant JE, et al. Antiretroviral Drugs for Treatment and Prevention of HIV Infection in Adults: 2016 Recommendations of the International Antiviral Society-USA Panel. Jama. 2016;316(2):191–210. pmid:27404187
- 37. Solomon MM, Mayer KH, Glidden DV, Liu AY, McMahan VM, Guanira JV, et al. Syphilis predicts HIV incidence among men and transgender women who have sex with men in a preexposure prophylaxis trial. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2014;59(7):1020–6. pmid:24928295
- 38. Holtgrave D, McGuire J. Impact of counseling in voluntary counseling and testing programs for persons at risk for or living with HIV infection. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2007;45 Suppl 4:S240–3. pmid:18190293
- 39. Ferguson H, Bovaird S, Mueller M. The impact of poverty on educational outcomes for children. Paediatrics & child health. 2007;12(8):701–6. pmid:19030450
- 40. Devoe JE, Baez A, Angier H, Krois L, Edlund C, Carney PA. Insurance + access not equal to health care: typology of barriers to health care access for low-income families. Annals of family medicine. 2007;5(6):511–8. pmid:18025488
- 41. Cerda M, Diez-Roux AV, Tchetgen ET, Gordon-Larsen P, Kiefe C. The relationship between neighborhood poverty and alcohol use: estimation by marginal structural models. Epidemiology. 2010;21(4):482–9. pmid:20498603
- 42. Valdez A, Kaplan CD, Curtis RL Jr. Aggressive crime, alcohol and drug use, and concentrated poverty in 24 U.S. urban areas. The American journal of drug and alcohol abuse. 2007;33(4):595–603. pmid:17668345
- 43. McCarthy B, Benoit C, Jansson M. Sex work: a comparative study. Archives of sexual behavior. 2014;43(7):1379–90. pmid:24671729
- 44. Rothenberg RB. The geography of gonorrhea. Empirical demonstration of core group transmission. American journal of epidemiology. 1983;117(6):688–94. pmid:6859024
- 45. Ghani AC, Garnett GP. Risks of acquiring and transmitting sexually transmitted diseases in sexual partner networks. Sexually transmitted diseases. 2000;27(10):579–87. pmid:11099073
- 46. Summaries for patients. Screening for chlamydia and gonorrhea: U.S. Preventive Services Task Force recommendation statement. Annals of internal medicine. 2014;161(12):I–30. pmid:25243662
- 47. Taylor MM, Korenromp E, Wi T. Pathways and progress to enhanced global sexually transmitted infection surveillance. PLoS medicine. 2017;14(6):e1002328. pmid:28654637
- 48. Goyal MK, Witt R, Hayes KL, Zaoutis TE, Gerber JS. Clinician adherence to recommendations for screening of adolescents for sexual activity and sexually transmitted infection/human immunodeficiency virus. The Journal of pediatrics. 2014;165(2):343–7. pmid:24840761
- 49. Adekeye OA, Abara WE, Xu J, Lee JM, Rust G, Satcher D. HIV Screening Rates among Medicaid Enrollees Diagnosed with Other Sexually Transmitted Infections. PloS one. 2016;11(8):e0161560. pmid:27556925
- 50. Lutz AR. Screening for Asymptomatic Extragenital Gonorrhea and Chlamydia in Men Who Have Sex with Men: Significance, Recommendations, and Options for Overcoming Barriers to Testing. LGBT health. 2015;2(1):27–34. pmid:26790015
- 51. Barbee LA, Dhanireddy S, Tat SA, Marrazzo JM. Barriers to Bacterial Sexually Transmitted Infection Testing of HIV-Infected Men Who Have Sex With Men Engaged in HIV Primary Care. Sexually transmitted diseases. 2015;42(10):590–4. pmid:26372931