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Observational Study
. 2016 Jun;95(26):e3961.
doi: 10.1097/MD.0000000000003961.

Analysis of contemporary HIV/AIDS health care costs in Germany: Driving factors and distribution across antiretroviral therapy lines

Affiliations
Observational Study

Analysis of contemporary HIV/AIDS health care costs in Germany: Driving factors and distribution across antiretroviral therapy lines

Marina Treskova et al. Medicine (Baltimore). 2016 Jun.

Abstract

To analyze contemporary costs of HIV health care and the cost distribution across lines of combination antiretroviral therapy (cART). To identify variations in expenditures with patient characteristics and to identify main cost determinants. To compute cost ratios between patients with varying characteristics.Empirical data on costs are collected in Germany within a 2-year prospective observational noninterventional multicenter study. The database contains information for 1154 HIV-infected patients from 8 medical centers.Means and standard deviations of the total costs are estimated for each cost fraction and across cART lines and regimens. The costs are regressed against various patient characteristics using a generalized linear model. Relative costs are calculated using the resultant coefficients.The average annual total costs (SD) per patient are &OV0556;22,231.03 (8786.13) with a maximum of &OV0556;83,970. cART medication is the major cost fraction (83.8%) with a mean of &OV0556;18,688.62 (5289.48). The major cost-driving factors are cART regimen, CD4-T cell count, cART drug resistance, and concomitant diseases. Viral load, pathology tests, and demographics have no significant impact. Standard non-nucleoside reverse transcriptase inhibitor-based regimens induce 28% lower total costs compared with standard PI/r regimens. Resistance to 3 or more antiretroviral classes induces a significant increase in costs.HIV treatment in Germany continues to be expensive. Majority of costs are attributable to cART. Main cost determinants are CD4-T cells count, comorbidity, genotypic antiviral resistance, and therapy regimen. Combinations of characteristics associated with higher expenditures enhance the increasing effect on the costs and induce high cost cases.

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Conflict of interest statement

Conflicts of Interest and Source of Funding: CORSAR study has been funded by an unrestricted Janssen-Cilag grant.

HHeiken is currently a board member by Abbvie, Bristol-Myers-Squibb, Gilead, MSD, Janssen-Cilag and ViiV Healthcare. He has received payment for lectures from Bristol-Myers Squibb, Gilead, MSD, and ViiV Healthcare, and payment for manuscript preparation from Gilead. MT, AK, and J-MGvdS declared no conflict of interest.

Figures

Figure 1
Figure 1
Spider web plot of cost ratios between patients with varying characteristics. Points on the axis give either increasing or decreasing effects of the presented groups of patient characteristics relative to the reference case for men (left) and women (right). Point types represent the respective therapy classes. The blue rhombus in the middle of the plot (left) gives the reference case, which corresponds to a cost ratio of 1 and the following characteristics: male, therapy class = “PI-stand,” CD4 = “>500,” comorbidity = “≤2 nonsevere,” drug resistance = “no resistance.” All other ratios (including those given on the plot for women) are presented relative to the reference case. Res, drug resistance; CD4, CD4-T cells count group; Com, comorbidity (categories are described in Table 1 ).

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