Pin38 the Cost of Stigma Associated With Hiv: A Cost-Consequence Analysis to Evaluate Hiv Testing and Prevention Interventions in Ksa

Abstract

The study assesses the global annual cost of people living with HIV (PLWHIV) in France in 2013 and affiliated to the general insurance scheme during 2008-2015. Methods: We performed a retrospective cohort study in the SNIIRAM database containing individualized data on all reimbursed health expenses for the French population. PLWHIV were identified by specific chronic diseases status and/or reimbursement of HIV laboratory tests and/or HIV-related hospitalizations and/or reimbursements of antiretroviral drugs in 2013. Each patient was followed two years after his inclusion. Hospital and outpatient costs were estimated and categorized in 13 subgroups: antiretroviral drugs, daily sick leave benefits, other drugs, paramedical visits, general practitioner visits, specialist visits, medical devices, transports, laboratory tests, other outpatient costs, hospital stays, hospital outpatient consultations and hospital at home. Costing was performed considering French official tariffs for 2013 and 2014 and expressed in 2019 euros. Results: A cohort of 96,423 PLWHIV was identified. Mean age was 47.5 years old (612), 65% were men, and most frequent treatments outside HIV drugs were treatments for cardiovascular disease (20%), psychotropic treatments (16%) and liver disease (15%). The total annual cost of PLWHIV was calculated to V1.4 billion, split into antiretroviral drugs for 63% (870MV), hospital stays for 11% (154MV), daily sick leave benefits for 8% (116MV), other drugs for 5% (73MV). The median annual cost per patient was estimated to be V12,094 [610,801, Q1: 9,001, Q3: 16,795], varying from V618 for 0-10 years old PLWHIV to V13,725 for 51-60 years old PLWHIV, V11,969 for incident patients (N=3,373), and V13,560 for patients with at least one comorbidity. Conclusions: This study shows that the global annual cost of PLWHIV is mainly charged to antiretroviral drugs cost. Only thin disparities in total cost were observed in each group (age, incident, comorbidity), advanced analyses such as Machine Learning could identify significantly overexpressing cost profiles.