***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: BACKGROUND The results of international clinical trials that are assessing when to initiate antiretroviral therapy ( ART ) will not be available for several years . OBJECTIVE To inform HIV treatment decisions about the optimal CD4 threshold at which to initiate ART in South Africa while awaiting the results of these trials . DESIGN Cost-effectiveness analysis by using a computer simulation model of HIV disease . DATA SOURCES Published data from r and omized trials and observational cohorts in South Africa . TARGET POPULATION HIV-infected patients in South Africa . TIME HORIZON 5-year and lifetime . PERSPECTIVE Modified societal . INTERVENTION No treatment , ART initiated at a CD4 count less than 0.250 x 10(9 ) cells/L , and ART initiated at a CD4 count less than 0.350 x 10(9 ) cells/L. OUTCOME MEASURES Morbidity , mortality , life expectancy , medical costs , and cost-effectiveness . RESULTS OF BASE-CASE ANALYSIS If 10 % to 100 % of HIV-infected patients are identified and linked to care , a CD4 count threshold for ART initiation of 0.350 x 10(9 ) cells/L would reduce severe opportunistic diseases by 22,000 to 221,000 and deaths by 25,000 to 253,000 during the next 5 years compared with ART initiation at 0.250 x 10(9 ) cells/L ; cost increases would range from $ 142 million ( 10 % ) to $ 1.4 billion ( 100 % ) . Either ART initiation strategy would increase long-term survival by at least 7.9 years , with a mean per-person life expectancy of 3.8 years with no ART and 12.5 years with an initiation threshold of 0.350 x 10(9 ) cells/L. Compared with an initiation threshold of 0.250 x 10(9 ) cells/L , a threshold of 0.350 x 10(9 ) cells/L has an incremental cost-effectiveness ratio of $ 1200 per year of life saved . RESULTS OF SENSITIVITY ANALYSIS Initiating ART at a CD4 count less than 0.350 x 10(9 ) cells/L would remain cost-effective over the next 5 years even if the probability that the trial would demonstrate the superiority of earlier therapy is as low as 17 % . LIMITATION This model does not consider the possible benefits of initiating ART at a CD4 count greater than 0.350 x 10(9 ) cells/L or of reduced HIV transmission . CONCLUSION Earlier initiation of ART in South Africa will probably reduce morbidity and mortality , improve long-term survival , and be cost-effective . While awaiting trial results , treatment guidelines should be liberalized to allow initiation at CD4 counts less than 0.350 x 10(9 ) cells/L , earlier than is currently recommended . PRIMARY FUNDING SOURCE National Institute of Allergy and Infectious Diseases and the Doris Duke Charitable Foundation Context Trials have not answered questions about optimal timing of HIV therapy , and many benefits and harms of therapy occur over time horizons that are longer than the trials . Early treatment may postpone AIDS , but it may also increase resistance and unnecessarily subject patients to the costs and toxicity of drugs . Contribution This computer simulation estimated that starting treatment earlier ( CD4 count threshold of 500 cells/mm3 ) would provide greater unadjusted and quality -adjusted life expectancy than starting it later ( at a CD4 count of 350 or 200 cells/mm3 ) for all patients age 30 years and for patients age 40 years who have a viral load greater than 30000 copies/mL. Caution Increases in life expectancy were small , and the analysis did not consider costs . The Editors The optimal timing for initiating HIV treatment has long been controversial . Soon after combination antiretroviral therapies became available , the prevailing treatment ethos was hit early , hit hard , and patients often began receiving therapy immediately after HIV infection was diagnosed ( 1 ) . However , as it became clear that toxicity and side effects from combination antiretroviral therapy were substantial ( 25 ) and that early treatment may hasten genotypic resistance ( 68 ) , treatment guidelines increasingly have favored postponing therapy initiation ( 9 , 10 ) . Yet , postponing therapy has disadvantages because it exposes HIV-infected patients to a greater risk for AIDS than is otherwise necessary ( 915 ) and may impair immune reconstitution ( 16 ) . Guidelines once again may be in flux , trending toward earlier treatment initiation ( 17 ) . The controversy about when to initiate HIV treatment has persisted because it has been difficult to systematic ally and quantitatively weigh the benefits and harms of earlier treatment ( 17 ) . Furthermore , because benefits and harms may be delayed ( for example , the accrual of genotypic resistance ) ( 8) , they must be estimated over time horizons that exceed usual follow-up periods for clinical trials . Mathematical models offer the possibility of quantitatively weighing harms and benefits over long time horizons , and therefore have theoretical promise for informing this question . However , published mathematical models addressing this question have not considered toxicity , side effects , and accrual of resistance mutations , the primary risks of earlier treatment initiation ( 1821 ) . Furthermore , they have not considered how the balance of risks to benefits may vary with advancing age because of changes in antiretroviral toxicity or immune reconstitution . For these reasons , we incorporated toxicity and side effects of combination antiretroviral therapy  into our previously vali date d HIV computer simulation , which considers resistance mutations and their likelihood of causing premature antiretroviral failure ( 2224 ) . We used the simulation to evaluate how these tradeoffs influence the optimal time to initiate therapy . Methods We first describe how we quantified the potentially harmful consequences of combination antiretroviral therapy and then discuss how we incorporated these estimates into our HIV simulation . Estimating the Harmful Consequences of Combination Antiretroviral Therapy We partitioned the harmful effect of combination antiretroviral therapy into 2 quantifiable categories : 1 ) a harmful effect on quantity of life due to toxicity and amplified risks for common comorbid events ( for example , myocardial infa rct ion ) ( 25 ) , manifested by higher nonHIV-related mortality and 2 ) a harmful effect on quality of life due to side effects , manifested by decreased scores on a preference-based quality -of-life measure ( 25 ) . Estimating the Effect of Combination Antiretroviral Therapy on NonHIV-Related Mortality It is not possible to directly estimate the effect of combination antiretroviral therapy on nonHIV-related mortality because there has been no r and omized , controlled trial with requisite statistical power . Large observational cohorts of HIV-infected patients have ample statistical power to address this question by comparing nonHIV-related mortality among individuals receiving with those not receiving combination antiretroviral therapy . However , confounding by treatment assignment may bias these results substantially , and the direction of this bias may be unclear . We therefore conducted our own analysis of mortality among all HIV-infected individuals receiving care within the Veterans Health Administration nationwide , together with a sample of uninfected Veterans Health Administration controls , matched 2:1 by age , race , and site . This virtual cohort includes in patients as well as out patients and patients receiving subspecialty care as well as those receiving primary care . It was created by integrating data from pharmacy , laboratory , and administrative sources from 1997 through 2004 . The creation of this virtual cohort is described in detail elsewhere ( 26 ) and has been shown to identify HIV-infected veterans with a sensitivity of 90 % and a specificity of 99.9 % ( 26 ) . We limited our analysis to HIV-infected individuals who were receiving combination antiretroviral therapy ( 3 drugs ) and were at exceptionally low risk for HIV-related death . We then compared the mortality of HIV-infected patients who met these inclusion criteria with that of their matched uninfected controls . Our rationale in making this comparison was that excess mortality observed in the HIV-infected group could be viewed as an upper limit for the nonHIV-related mortality attributable to combination antiretroviral therapy because it would reflect nonHIV-related mortality as well as other factors ( for example , residual mortality attributable to HIV ) . We decided not to analyze only HIV-infected patients because we did not think that we could fully control for intention-to-treat bias and because we would not know the direction of this bias ( that is , we would not know that the result was accurate or that it bounded the true value in a particular direction ) . We selected individuals at low risk for death related to HIV by specifying a minimum threshold for their time-up date d CD4 count ( that is , individuals could contribute a particular interval of observation to the analysis only if they remained above the CD4 count threshold during that interval ) . We used a threshold of 500 cells/mm3 because it is uncommon for individuals with higher CD4 counts to die of an AIDS-related cause ( 27 ) . We also explored alternative analyses using thresholds based on minimum CD4 count rather than time-varying CD4 count ( 9 ) , but this did not affect our results substantially . We performed Cox proportional hazards models to compare the mortality of the HIV-infected patients with that of their matched uninfected controls , including the covariates age , sex , race , site of care , and presence or absence of the most common serious comorbid conditions in this sample ( coronary artery disease , congestive heart failure , diabetes mellitus , hepatitis C , pancreatitis , peripheral vascular disease , pulmonary disease , stroke , pneumonia , and non-AIDS cancer ) . Diagnoses were identified by using a previously vali date d algorithm based on International Classification of Diseases , Ninth Revision , administrative codes in the inpatient and outpatient setting s ( 26 ) . To avoid overadjusting for comorbid conditions , we required comorbidity diagnoses to have been made before combination antiretroviral therapy was initiated . We did not stratify combination antiretroviral therapy by type ( for example , regimens with protease inhibitors versus regimens with nonnucleoside reverse transcriptase inhibitors ) because this would have greatly limited our statistical power . Because there appeared to be a substantially higher mortality hazard in the short term after the start of combination antiretroviral therapy ( during the first 3 months ) than over the long term , we considered this short-term hazard separately . We judged this high short-term hazard as probably due to confounding ( that is , attributable to an acute clinical syndrome that may have prompted the initiation of care ) rather than to combination antiretroviral therapy toxicity , and therefore we did not incorporate it into the simulation 's base-case analysis . However , we also explored alternative analyses in which we did attribute this high short-term hazard to toxicity , and this did not affect our results substantially . Among 33420 HIV-infected patients in the virtual cohort , 9633 ( 29 % ) had infrequent or missing CD4 counts ( > 1 year between successive CD4 measurements ) , and an additional 18045 did not meet inclusion criteria ( no combination antiretroviral therapy , or CD4 count always < 500 cells/mm3 ) , leaving 5742 HIV-infected patients along with 11484 controls ( 17226 patients in total ) in our analysis . The mean age of patients and controls was 45.7 years , 98.0 % were men , and 68.9 % were nonwhite . We found that combination antiretroviral therapy was associated with a 3.4-fold increased long-term hazard of nonHIV-related mortality in unadjusted analyses ( 95 % CI , 2.8-fold to 4.1-fold ) and a 3.8-fold increased hazard in adjusted analyses ( CI , 3.1-fold to 4.6-fold ) ( Table 1 ) . Because this association is likely to encompass factors other than toxicity of combination antiretroviral therapy , it is an upper-bound estimatethat is , the true magnitude of the effect of combination antiretroviral therapy is likely to be lower . We included this estimate in the simulation model with the underst and ing that it biases the model in a known , conservative direction ( against showing a benefit from earlier treatment initiation ) . Table 1 . Analysis to Estimate Upper Bound for Mortality from Therapy-Related Toxicity : Predictors of Mortality among HIV-Infected Veterans and Matched Uninfected Controls Nationwide in the Veterans Affairs Health System * Estimating the Effect of Combination Antiretroviral Therapy on Quality of Life We estimated the effect of combination antiretroviral therapy on quality of life BACKGROUND Despite declines in morbidity and mortality with the use of combination antiretroviral therapy , its effectiveness is limited by adverse events , problems with adherence , and resistance of the human immunodeficiency virus ( HIV ) . METHODS We r and omly assigned persons infected with HIV who had a CD4 + cell count of more than 350 per cubic millimeter to the continuous use of antiretroviral therapy ( the viral suppression group ) or the episodic use of antiretroviral therapy ( the drug conservation group ) . Episodic use involved the deferral of therapy until the CD4 + count decreased to less than 250 per cubic millimeter and then the use of therapy until the CD4 + count increased to more than 350 per cubic millimeter . The primary end point was the development of an opportunistic disease 
    Output:
    
There is evidence of moderate quality that initiating ART at CD4 levels higher than 200 or 250 cells/microL reduces mortality rates in asymptomatic , ART-naive , HIV-infected people .