Examining Trends in Sexually Transmitted Infections by Linkages of Secondary Data Sources

The incidence of chlamydia, gonorrhea, and early syphilis have increased in the U.S. since 2014 after a period of relative stability. The factors associated with this increase in incidence varies by geography and are multifactorial. There are few studies that examine this time period discretely (i.e., before 2014, 2014 and onward) to identify any changes in sociodemographic trends among chlamydia, gonorrhea, and early syphilis cases. Additionally, the COVID-19 pandemic had a profound effect on sexually transmitted infections (STI) control efforts by the diversion of STI laboratory testing materials and STI control staff at health departments toward the COVID-19 response. The effects of this diversion of resources and the shelter-in-place order in San Mateo County and its surrounding counties on STI incidence and detection are still not well understood. Since the start of the COVID-19 pandemic, the incidence of congenital syphilis cases in San Mateo County, the state of California, and the U.S. have increased at an alarming rate. Congenital syphilis is considered to be fully preventable and, thus, a sentinel event to identify failures in the public health and health care delivery system. Health departments have access to a vast number of data sources that can identify and describe the sociodemographic characteristics of STI cases, including those who gave birth to infants with congenital syphilis, but there is often a lack of resources to fully investigate the association between upstream factors, such as neighborhood effects, and STIs. One such measure that is used by the California Department of Public Health (CDPH) and local health departments was the Health Places Index (HPI), a composite measure that encompasses several aspects of neighborhood quality and opportunity. Although the HPI is an ecological-level variable, it can provide information about neighborhood context in the absence of any individual-level socioeconomic status information./nThis dissertation leveraged the multiple data sources available at a norther California public health department by using deterministic and probabilistic linkage to combine data from the California Reportable Disease Information Exchange (CalREDIE), the San Mateo Medical Center, the San Mateo County Public Health Laboratory, and birth records from the California Department of Public Health—Vital Records from 2010 to 2021. Although CalREDIE, a reportable disease registry, contains information about chlamydia, gonorrhea, and early syphilis cases, the demographic information captured in CalREDIE can be inconsistent in quality and completeness. This linkage allowed us (1) to impute any missing race/ethnicity values in CalREDIE from other data sources which yielded more accurate calculations (2) join information from other secondary data sources to enrich an existing dataset (e.g., joining HPI scores to CalREDIE) or to identify unique pregnancies (e.g., joining birth records to San Mateo Medical Center records). Three studies were conducted with three datasets created from this linkage process. The first study described the trend of the incidence of chlamydia, gonorrhea, and early syphilis in San Mateo County, CA from 2010 to 2021 using a dataset created from linking CalREDIE data to hospital and laboratory records. The second study conducted a retrospective cohort analysis that examined the association between sociodemographic factors, including HPI, and chlamydia and gonorrhea reinfection from 2010 to 2021 using a dataset derived from the first study. The third study examined the change in the proportion of syphilis testing among pregnant individuals who received prenatal care at the San Mateo Medical Center before and during the COVID-19 pandemic, using San Mateo Medical Center records linked to birth records to identify unique pregnancies and to link sociodemographic information that was not available in the San Mateo Medical Center records./nThe methodology and findings are intended to provide a blueprint for health departments to harness the many data sources available to them as the analyses conducted in this dissertation can be readily replicated and applied to other health outcomes.

Advisor: Reingold, Arthur L