11 research outputs found

    Healthcare utilization and direct costs of non-infectious comorbidities in HIV-infected patients in the USA

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    <p><b>Objective:</b> To estimate the incremental healthcare utilization and costs associated with common non-infectious comorbid conditions among commercially and Medicaid-insured HIV-infected patients in the US.</p> <p><b>Methods:</b> US administrative claims were used to select adult HIV patients with chronic kidney disease (CKD), cardiovascular disease (CVD) events, or fracture/osteoporosis, three common comorbidities that have been associated with HIV and HIV treatment, between 1 January 2004 and 30 June 2013. Propensity score matched controls with no CKD, no CVD events, and no fracture/osteoporosis were identified for comparison. All-cause healthcare utilization and costs were reported as per patient per month (PPPM).</p> <p><b>Results:</b> The commercial cohort comprised 381 CKD patients, 624 patients with CVD events, and 774 fracture/osteoporosis patients, and 1013, 1710, and 2081 matched controls, respectively; while the Medicaid HIV cohort comprised 207 CKD and 271 CVD cases, and 516 and 735 matched controls, respectively. There was insufficient Medicaid data for fracture analyses. Across both payers, HIV patients with CKD or CVD events had significantly higher healthcare utilization and costs than controls. The average incremental PPPM costs in HIV patients with CKD were 1403inthecommercialcohortand1403 in the commercial cohort and 3051 in the Medicaid cohort. In those with CVD events, the incremental costs were 2655(commercial)and2655 (commercial) and 4959 (Medicaid) for HIV patients compared to controls (<i>p</i> < .001).</p> <p><b>Conclusions:</b> The results suggested a considerable increase in healthcare utilization and costs associated with CKD, CVD and fracture/osteoporosis comorbidities among HIV patients in the past decade. Because these conditions have been associated with treatment, it is critical to consider their impact on costs and outcomes when optimizing patient care.</p

    CMV-specific T cell responses in HIV uninfected and infected adults.

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    <p>NOTE. Median values and interquartile ranges are shown. All subjects contributed pp65-specific IFN-γ-bright data (n = 685), while only a subset contributed pp65 and IE data (see text). IE data were not available from the recently infected subjects. The combined pp65 and IE interferon-γ/IL2 data reflect the sum of all possible response (IFN-γ alone, IL2 alone and IFN-γ/IL2) to each of the CMV protein.</p

    The distribution of the CMV-specific CD4+ and CD8+ T cell responses (background corrected) in three unique groups: (1) HIV-seronegative, CMV-seropositive, (2) established chronic untreated HIV infection, and (3) antiretroviral-treated infection with undetectable plasma HIV RNA levels.

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    <p>The IE IFN-γ-bright levels are shown in panel A (CD4+ T cells) and panel B (CD8+ T cells). The combined pp65 and IE IFN-γ/IL2 data are shown in panels C (CD4+ T cells) and panel D (CD8+ T cells). The data in panels C and D reflect the sum of all possible response (IFN-γ alone, IL2 alone, or IFN-γ/IL2) to each of the CMV proteins. Standard gating was used for dual cytokine data.</p

    The distribution of pp65 IFN-γ-bright CD4+ and CD8+ T cell responses (background corrected) in four unique groups: (1) HIV-seronegative, CMV-seropositive, (2) acute and recent untreated HIV infection, (3) established chronic untreated HIV infection, and (4) antiretroviral-treated infection with undetectable plasma HIV RNA levels.

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    <p>The distribution of pp65 IFN-γ-bright CD4+ and CD8+ T cell responses (background corrected) in four unique groups: (1) HIV-seronegative, CMV-seropositive, (2) acute and recent untreated HIV infection, (3) established chronic untreated HIV infection, and (4) antiretroviral-treated infection with undetectable plasma HIV RNA levels.</p

    Basic schematic of the model design.

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    <p>The model follows patients on antiretroviral therapy from 2010 to 2035 or death. The model simulates how patients age over time and develop clinical events, including non-communicable diseases or death. The model takes into account key interactions between demographic factors (blue), e.g. how age and gender can impact risk of death, and clinical factors (green) e.g how hypertension can increase the risk of chronic kidney disease or death. <i>Adapted from source</i>: <i>Smit et al</i> [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186638#pone.0186638.ref009" target="_blank">9</a>]. <i>Abbreviations</i>: <i>Myocardial Infarction (MI)</i>, <i>Chronic Kidney Disease (CKD)</i>.</p
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