Objective Enhanced HIV prevention interventions, such as pre-exposure prophylaxis for high-risk individuals, require substantial investments. We sought to estimate the medical cost saved by averting one HIV infection in the United States. Methods We estimated lifetime medical costs in persons with and without HIV to determine the cost saved by preventing one HIV infection. We used a computer simulation model of HIV disease and treatment (CEPAC) to project CD4 cell count, antiretroviral treatment status, and mortality after HIV infection. Annual medical cost estimates for HIV-infected persons, adjusted for age, sex, race/ethnicity, and transmission risk group, were from the HIV Research Network (range $1,854–$4,545/month) and for HIV-uninfected persons were from the Medical Expenditure Panel Survey (range $73–$628/month). Results are reported as lifetime medical costs from the US health system perspective discounted at 3% (2012 US dollars). Results The estimated discounted lifetime cost for persons who become HIV infected at age 35 is $326,500 (60% for antiretroviral medications, 15% for other medications, 25% non-drug costs). For individuals who remain uninfected but at high risk for infection, the discounted lifetime cost estimate is $96,700. The medical cost saved by avoiding one HIV infection is $229,800. The cost saved would reach $338,400 if all HIV-infected individuals presented early and remained in care. Cost savings are higher taking into account secondary infections avoided and lower if HIV infections are temporarily delayed rather than permanently avoided. Conclusions The economic value of HIV prevention in the US is substantial given the high cost of HIV disease treatment.

Emily Hyle and colleagues conduct a cost-effectiveness analysis to estimate the clinical and economic impact of point-of-care CD4 testing compared to laboratory-based tests in Mozambique. Please see later in the article for the Editors' Summary

Unprecedented investments in health systems low- and middle-income countries (LMICs) have resulted in more than eight million individuals on antiretroviral therapy (ART). Such individuals experience dramatically increased survival, but are increasingly at risk of developing common non-communicable diseases (NCDs). Integrating clinical care for HIV, other infectious diseases, and NCDs could make health services more effective and provide greater value. Cost-effectiveness analysis is a method to evaluate the clinical benefits and costs associated with different healthcare interventions and offers guidance for prioritization of investments and scale-up, especially as resources are increasingly constrained. We first examine tuberculosis and HIV as one example of integrated care already successfully implemented in several LMICs; we then review the published literature regarding cervical cancer and depression as two examples of NCDs for which integrating care with HIV services could offer excellent value. Direct evidence of the benefits of integrated services generally remains scarce; however, data suggest that improved effectiveness and reduced costs may be attained by integrating additional services with existing HIV clinical care. Further investigation into clinical outcomes and costs of care for NCDs among people living with HIV (PLHIV) in LMICs will help to prioritize specific healthcare services by contributing to an understanding of the affordability and implementation of an integrated approach.

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Purpose Network analysis has become increasingly popular in epidemiologic research, but the accuracy of data key to constructing risk networks is largely unknown. Using network data from people who use drugs (PWUD), the study examined how accurately PWUD reported their network members’ (i.e., alters’) names and ages. Methods Data were collected from 2008 to 2010 from 503 PWUD residing in rural Appalachia. Network ties (n=897) involved recent (past 6 months) sex, drug co-usage, and/or social support. Participants provided alters’ names, ages, and relationship-level characteristics; these data were cross-referenced to that of other participants to identify participant-participant relationships and to determine the accuracy of reported ages (years) and names (binary). Results Participants gave alters’ exact names and ages within two years in 75% and 79% of relationships, respectively. Accurate name was more common in relationships that were reciprocally reported and those involving social support and male alters. Age was more accurate in reciprocal ties and those characterized by kinship, sexual partnership, recruitment referral, and financial support, and less accurate for ties with older alters. Conclusions Most participants reported alters’ characteristics accurately, and name accuracy was not significantly different in relationships involving drug-related/sexual behavior compared to those not involving these behaviors.