Using Community-Based Data Collection to Augment HBV Prevalence Estimates in Asian and Pacific Islander Communities in Philadelphia

Background/Significance: Chronic hepatitis B (CHB) is a serious liver infection caused by the hepatitis B virus (HBV). Two million people in the US have CHB, of which 50% are of API descent, and 25% will die prematurely of cirrhosis or liver cancer if unmanaged. Up to 75% of infected APIs in the U.S. remain unaware of their CHB, putting them at greater risk of morbidity and mortality. Due to the lack of systematic surveillance, and the lack of API inclusion in surveillance, CHB infection rates among APIs remain dramatically underestimated.

Purpose: This study proposes community-based data collection to augment existing surveillance. We collected individual-level, disaggregated CHB data on APIs in Philadelphia to provide community-based estimates of prevalence with critical information on associated risk factors.

Methods: Community-based HBV screenings were conducted in high-risk neighborhoods. An IRB-approved survey collected information on demographics, healthcare usage, and past/family history of CHB. Blood samples confirmed HBV infection status. Data were analyzed using SAS 9.3. Descriptive statistics, frequency analysis, and logistic regression were used to describe the participant population and determine risk factors associated with HBV infection, susceptibility and linkage to care.

Results: Of 2,322 people, 58% were female; 97% were foreign-born; 87% were limited-English proficient; 36% had health insurance; 3% knew of a family member having HBV; 19% reported being previously screened; 7% tested positive for infection; and 66% were protected. A final risk model was built based using logistic regression.

Discussion: Data collected at community events, in addition to routinely-collected surveillance data, can help understand the picture of local disease prevalence and healthcare usage among APIs. These data will help us develop more successful targeted interventions to address the needs of the community. The data can also be used for local and national advocacy, to help prioritize API health disparities for the future.