Adjusting survival estimates by incorporating loss to follow-up in antiretroviral therapy programs in sub-Saharan Africa.
ABSTRACT Background. Measuring the survival of adult patients in antiretroviral therapy (ART) programs is complicated by short observation periods and patients lost-to-follow-up (LTFU). We synthesized data from sub-Saharan Africa (SSA) treatment cohorts to estimate survival over five years after initiating ART.Methods. We used data on retention, mortality and loss to follow-up, extracted from 34 cohorts, including a total of 102,306 adult patients from 18 SSA countries, augmented by 13 SSA studies tracking death rates among adult patients LTFU. We used a Poisson regression model to estimate survival over time, incorporating predicted mortality among LTFU patients.Results. Across studies median CD4 count at ART initiation was 104 cells/mm(3), 65% of patients were female, and median age was 37 years. 1-year and 5-year survival, adjusted for loss to follow-up, were estimated at 0.87 (95% C.I: 0.72-0.94) and 0.70 (0.36-0.86), respectively. The life-years gained by a patient over five years after starting treatment were estimated at 2.1 (1.6-2.3) in the adjusted model, compared to 1.7 (1.1-2.0) if assuming 100% mortality among LTFU patients, or 2.4 (1.7-2.7) assuming 0% mortality among LTFU patients.Conclusions. Accounting for loss to follow-up produces substantial changes in the estimated life-years gained during the first five years on ART.
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ABSTRACT: There is evidence that the life expectancy (LE) of individuals infected with the human immunodeficiency virus (HIV) has increased since the introduction of combination antiretroviral therapy (cART). However, mortality rates in recent years in HIV-positive individuals appear to have remained higher than would be expected based on rates seen in the general population. A low CD4 count, whether due to late HIV diagnosis, late initiation of cART, or incomplete adherence to cART, remains the dominant predictor of LE, and thus the individual's disease stage at initiation of cART (or thereafter) certainly contributes to these higher mortality rates. However, individuals with HIV also tend to exhibit lifestyles and behaviors that place them at increased risk of mortality, particularly from non-AIDS causes. Thus, although mortality rates among the HIV population may indeed remain slightly higher than those seen in the general population, they may be no higher than those seen in a more appropriately matched control group. Thus, further improvements in LE may now only be possible if some of the other underlying issues (for example, modification of lifestyle or behavioral factors) are tackled.BMC Medicine 01/2013; 11(1):251. DOI:10.1186/1741-7015-11-251 · 7.28 Impact Factor
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ABSTRACT: Background The Millennium Declaration in 2000 brought special global attention to HIV, tuberculosis, and malaria through the formulation of Millennium Development Goal (MDG) 6. The Global Burden of Disease 2013 study provides a consistent and comprehensive approach to disease estimation for between 1990 and 2013, and an opportunity to assess whether accelerated progress has occured since the Millennium Declaration. Methods To estimate incidence and mortality for HIV, we used the UNAIDS Spectrum model appropriately modified based on a systematic review of available studies of mortality with and without antiretroviral therapy (ART). For concentrated epidemics, we calibrated Spectrum models to fit vital registration data corrected for misclassification of HIV deaths. In generalised epidemics, we minimised a loss function to select epidemic curves most consistent with prevalence data and demographic data for all-cause mortality. We analysed counterfactual scenarios for HIV to assess years of life saved through prevention of mother-to-child transmission (PMTCT) and ART. For tuberculosis, we analysed vital registration and verbal autopsy data to estimate mortality using cause of death ensemble modelling. We analysed data for corrected case-notifications, expert opinions on the case-detection rate, prevalence surveys, and estimated cause-specific mortality using Bayesian meta-regression to generate consistent trends in all parameters. We analysed malaria mortality and incidence using an updated cause of death database, a systematic analysis of verbal autopsy validation studies for malaria, and recent studies (2010—13) of incidence, drug resistance, and coverage of insecticide-treated bednets. Findings Globally in 2013, there were 1·8 million new HIV infections (95% uncertainty interval 1·7 million to 2·1 million), 29·2 million prevalent HIV cases (28·1 to 31·7), and 1·3 million HIV deaths (1·3 to 1·5). At the peak of the epidemic in 2005, HIV caused 1·7 million deaths (1·6 million to 1·9 million). Concentrated epidemics in Latin America and eastern Europe are substantially smaller than previously estimated. Through interventions including PMTCT and ART, 19·1 million life-years (16·6 million to 21·5 million) have been saved, 70·3% (65·4 to 76·1) in developing countries. From 2000 to 2011, the ratio of development assistance for health for HIV to years of life saved through intervention was US$4498 in developing countries. Including in HIV-positive individuals, all-form tuberculosis incidence was 7·5 million (7·4 million to 7·7 million), prevalence was 11·9 million (11·6 million to 12·2 million), and number of deaths was 1·4 million (1·3 million to 1·5 million) in 2013. In the same year and in only individuals who were HIV-negative, all-form tuberculosis incidence was 7·1 million (6·9 million to 7·3 million), prevalence was 11·2 million (10·8 million to 11·6 million), and number of deaths was 1·3 million (1·2 million to 1·4 million). Annualised rates of change (ARC) for incidence, prevalence, and death became negative after 2000. Tuberculosis in HIV-negative individuals disproportionately occurs in men and boys (versus women and girls); 64·0% of cases (63·6 to 64·3) and 64·7% of deaths (60·8 to 70·3). Globally, malaria cases and deaths grew rapidly from 1990 reaching a peak of 232 million cases (143 million to 387 million) in 2003 and 1·2 million deaths (1·1 million to 1·4 million) in 2004. Since 2004, child deaths from malaria in sub-Saharan Africa have decreased by 31·5% (15·7 to 44·1). Outside of Africa, malaria mortality has been steadily decreasing since 1990. Interpretation Our estimates of the number of people living with HIV are 18·7% smaller than UNAIDS's estimates in 2012. The number of people living with malaria is larger than estimated by WHO. The number of people living with HIV, tuberculosis, or malaria have all decreased since 2000. At the global level, upward trends for malaria and HIV deaths have been reversed and declines in tuberculosis deaths have accelerated. 101 countries (74 of which are developing) still have increasing HIV incidence. Substantial progress since the Millennium Declaration is an encouraging sign of the effect of global action.The Lancet 07/2014; 384(9947):1005-1070. DOI:10.1016/S0140-6736(14)60844-8 · 39.21 Impact Factor
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ABSTRACT: A major challenge in monitoring universal health coverage (UHC) is identifying an indicator that can adequately capture the multiple components underlying the UHC initiative. Effective coverage, which unites individual and intervention characteristics into a single metric, offers a direct and flexible means to measure health system performance at different levels. We view effective coverage as a relevant and actionable metric for tracking progress towards achieving UHC. In this paper, we review the concept of effective coverage and delineate the three components of the metric - need, use, and quality - using several examples. Further, we explain how the metric can be used for monitoring interventions at both local and global levels. We also discuss the ways that current health information systems can support generating estimates of effective coverage. We conclude by recognizing some of the challenges associated with producing estimates of effective coverage. Despite these challenges, effective coverage is a powerful metric that can provide a more nuanced understanding of whether, and how well, a health system is delivering services to its populations.PLoS Medicine 09/2014; 11(9):e1001730. DOI:10.1371/journal.pmed.1001730 · 15.25 Impact Factor