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Home Research Research Library Primary care screening for sexually transmitted infections in the United States from 2019 to 2021 Primary care screening for sexually transmitted infections in the United States from 2019 to 2021 2025 Author(s) Hao, Shiying, Velásquez, Esther E, Pearson, William S, Hoover, Karen W, Zhu, Weiming, Rochlin, Ilia, Vala, Ayin, Chu, Isabella, Phillips, Robert L, Rehkopf, David H, and Kamdar, Neil S Volume PLOS One Source PLOS One Background Early identification and treatment of sexually transmitted infections (STIs) is critical to improve patient outcomes. Barriers to healthcare seeking are potentially exacerbated by COVID-19. This study examined trends in STI testing and positivity from 2019 to 2021 in primary care in the United States. Methods This is a retrospective study using the PRIME Registry, a national primary care EHR registry, from January 1, 2019-December 31, 2021. We calculated age-standardized monthly and annual testing rates for chlamydia, gonorrhea, syphilis, and human immunodeficiency virus stratified by gender and race/ethnicity. We also generated quarterly and annual rates for test positivity. Chi-square tests and 95% confidence intervals were used for comparison. 753 practices and 4,410,609 patients were included, with 180,558 having STI tests. Results We observed a substantial decline in testing rates for STIs from March-April 2020 (31% for chlamydia, 30% for gonorrhea, 23% for syphilis, 24% for HIV), followed by a rapid increase in May-June 2020 (64% for chlamydia, 65% for gonorrhea, 32% for syphilis, 48% for HIV). Testing rates per 100,000 decreased from 2019 to 2021 for chlamydia (3,592 vs 2,355 vs 2,181) while increased for gonorrhea in 2020 (2,129 vs 2,207 vs 2,057). STI testing rates from 2019 to 2021 for females and non-Hispanic Black or African American patients were higher than other groups. An increase in test positivity from 2019 to 2021 was observed for gonorrhea (0.4% vs 0.4% vs 0.5%) but no significant change for chlamydia (1.5% vs 1.6% vs 1.5%). Conclusion Testing rates for STIs substantially dropped during stay-at-home orders early in the pandemic and recovered after these were relaxed. Gender and race/ethnicity STI testing differences may reflect primary care’s prioritization of higher risk populations. This study emphasizes the role of primary care EHR data in monitoring and an opportunity for closer collaboration with public health agencies. ABFM Research Read all 2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare 2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence 2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
Author(s) Hao, Shiying, Velásquez, Esther E, Pearson, William S, Hoover, Karen W, Zhu, Weiming, Rochlin, Ilia, Vala, Ayin, Chu, Isabella, Phillips, Robert L, Rehkopf, David H, and Kamdar, Neil S Volume PLOS One Source PLOS One
ABFM Research Read all 2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare 2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence 2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality 2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
2025 Reclaiming Medical Professionalism In An Era Of Corporate Healthcare Go to Reclaiming Medical Professionalism In An Era Of Corporate Healthcare
2025 Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence Go to Leveraging Large Language Models to Advance Certification, Physician Learning, and Diagnostic Excellence
2025 Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality Go to Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality
2025 Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone Go to Natural Language Processing Improves Reliable Identification of COVID-19 Compared to Diagnostic Codes Alone
