1. 1 Lauren E. Finn, 2 Seth Sheffler-Collins, MPH, 2 Marcelo Fernandez-Viña, MPH, 2 Claire Newbern, PhD, 1 Dr. Alison Evans, ScD., 1 Drexel University School of Public Health, 2 Philadelphia Department of Public Health Acknowledgements This project was created with the assistance of Dr. Lucy Robinson at Drexel University School of Public Health. PHC4 is an independent state agency that provided data for this study. PHC4 specifically disclaims responsibility for any analyses, interpretations, or conclusions. After controlling for demographic characteristics, with the exception of HBV and HBV/HIV, viral co-infection leads to statistically significant increases in hospitalization rates relative to mono-infection. This finding highlights the importance of the prevention of secondary infections among mono- infected persons, both for individual health outcomes and for the health care system. The high frequency of hospitalization observed among all infection groups may indicate a need for better routine care as well as maintenance in care for infected persons. Large proportion of hospital visits linked to mental health care and drug abuse among infected patients may indicate a need for greater viral infection screening among patients seeking mental health or rehabilitation services. HIV, HBV, and HCV are prevalent infectious diseases Roughly 2.2 million people are infected with HBV 3.2 million are infected with HCV An additional 1.1 million are HIV-infected Co-infection with these diseases is common as well Approximately 25% of HIV patients are co-infected with HCV, while an additional 10% are co-infected with HBV Roughly 25% of HCV patients are co-infected with HBV There are gaps in knowledge on the impact of co-infection at a population level. OBJECTIVES: The purpose of this study was to examine the health care burdens associated with human immunodeficiency virus (HIV), hepatitis C (HCV), and hepatitis B (HBV) mono-infections and HIV/HCV, HBV/HCV, and HBV/HIV co-infections in southeastern Pennsylvania. Also, we assessed trends in hospitalization among infected individuals, as well as risk factors associated with hospitalization. METHODS: A retrospective cohort study utilizing hospitalization data for residents of southeastern Pennsylvania, extracted from the Pennsylvania Health Care Cost Containment Council (PHC4) hospital billing database for 1996-2010. Infection status was determined by ICD- 9 codes. The co-infection and mono-infection patient hospitalization rates were compared. RESULTS: 13,175 persons infected with HCV were identified, with a mean of 1.34 hospitalizations per patient per year. 1,055 HBV cases and 4,927 HIV cases were also found, with means of 1.59 and 0.98 visits per patient per year, respectively. Among the co-infection groups, 1,005 cases of HIV/HCV, 869 cases of HBV/HCV, and 295 cases of HIV/HBV were identified, with means of 1.73, 1.99, and 1.79 visits per patient per year, respectively. After controlling for demographic characteristics and other potential confounders, multivariate linear modeling suggested 18.4% (95% CI: 14.7-21.8%) and 41.1% (95% CI: 38.3-43.7%) decreases in visit rate associated with HCV and HIV mono-infections, respectively, relative to HIV/HCV co-infection. Compared to HBV/HCV co-infection, HBV mono-infection resulted in an 18.7% decrease in visit rate (95% CI: 13.7-23.5%), and HCV mono- infection similarly decreased visit rates by 32.0% (95% CI: 28.9-35.1%). Compared to HBV/HIV co-infection, HIV mono-infection resulted in a 41.3% reduction in visit rate (95% CI: 36.4-45.8%). However, a non- significant 4.2% reduction in visit rate was seen among HBV mono- infected patients relative to HBV/HIV co-infected cases. CONCLUSIONS: The observed increases in the rates of hospitalization for virally co-infected persons relative to mono-infected persons indicates a critical need for early identification and treatment of co- infected persons, as well as services integration at the screening and treatment levels. Utilization of Hospital Billing Data to Analyze Trends of Human Immunodeficiency Virus, Hepatitis C, and Hepatitis B Infection in Southeastern Pennsylvania Figure 3. Associations between hospital visit rate, infection status, & demographic characteristics among mono- and co-infected persons in PHC4 from 1996- 2010. Methods PHC4 Hospital Billing Database Pennsylvania Health Care Cost Containment Council (PHC4) collects hospital admissions data on a yearly basis Each patient assigned a unique pseudo-identifier linking visits across hospitals and years Collects demographic data, medical diagnosis codes, length of hospital stay, and associated hospital charges Subject Identification Mono- and co-infected patients identified through the use of ICD-9 coding within the PHC4 hospital billing database ICD-9 codes indicative of both infections were required during the same hospital visits to qualify as co-infected Study restricted to individuals aged 18 to 70 years old with available race information and at least three hospital visits with infection-associated ICD-9 codes, as shown in Figure 1 Univariate Analysis Examined differences in hospitalization rates between viral mono-infected and co-infected patients Calculated visit rates per person by dividing the total number of hospitalizations for each patient by his/her total follow-up time, for years 1996 to 2010 As time of initial infection was unknown and each infection is associated with increased morbidity from a number of causes beyond the initial infection, all visits linked to each patient were included in the visit rate Compared visit rate distribution between mono- and co- infected groups using Wilcoxon tests Examined demographic and temporal trends in hospitalization Identified primary causes of hospitalization among mono- and co-infected persons Multivariate Modeling Examined associations between hospital visit rate and infection status while adjusting for possible confounders Generalized linear modeling analysis used to assess associations and 95% confidence intervals (95% CIs) while adjusting for confounders Included variables for year of birth, race, ethnicity, gender, infection status, insurance status, urban/suburban area of residence, and year of first diagnosis Figure 2. Increases in hospital visit rate among virally co-infected persons relative to mono-infected persons within the PHC4 hospital billing database from 1996 to 2010. Associated Health Conditions Subject Selection Univariate Visit Rate Comparison Univariate comparison of visit rate data revealed increases in hospital visit rate among the co-infected groups relative to the mono-infected groups, as indicated in Figure 2. Figure 1. Sample selection process for HIV, HBV, HCB, HIV/HCV, HBV/HCV, and HBV/HIV infected persons within the PHC4 hospital billing database. Table 1. Top five diagnoses associated with hospitalization among persons infected with HCV, HBV, HIV, HIV/HCV, HBV/HCV, and HBV/HIV within the PHC4 hospital billing database from 1996 to 2010. Multivariate Modeling Results Background Abstract Multivariate modeling demonstrated a number of statistically significant associations between infection status and hospital visit rate (Figure 3). Compared to HIV/HCV co-infection HCV mono-infection resulted in a 18.4% decrease in visit rate (95% CI: 14.7-21.8%) HIV mono-infection resulted in a 41.1% decrease (95% CI: 38.3-43.7%) Relative to HBV/HCV co-infection HBV mono-infection resulted in an 18.7% decrease in visit rate (95% CI: 13.7-23.5%) HCV mono-infection similarly decreased visit rate by 32.0% (95% CI: 28.9-35.1%) Compared to HBV/HIV co-infection HIV mono-infection resulted in a 41.3% reduction in visit rate (95% CI: 36.4-45.8%) Non-significant 4.2% reduction in visit rate seen among HBV mono-infected patients  Determine hospitalization trends for individuals infected with HIV, HBV, HCV, HIV/HCV, HBV/HCV, and HBV/HIV in southeastern Pennsylvania from 1996 to 2010.  Examine associations between hospitalization and risk factors such as age, sex, race, ethnicity, area of residence, and insurance status.  Compare mono-infected and co-infected patients on the health care and financial burdens (including rate of hospitalization) associated with hospitalization.  Identify both previously missed and future opportunities for effective screening for these infections. Aims Conclusions & Recommendations