Estimating the Capacity for ART Provision in Tanzania with the Use of Data on Staff Productivity and Patient Losses

BACKGROUND: International targets for access to antiretroviral therapy (ART) have over-estimated the capacity of health systems in low-income countries in Sub-Saharan Africa. The WHO target for number on treatment by end 2005 for Tanzania was 10 times higher than actually achieved. The target of the national Care and Treatment Plan (CTP) was also not reached. We aimed at estimating the capacity for ART provision and created five scenarios for ART production given existing resource limitations. METHODS: A situation analysis including scrutiny of staff factors, such as available data on staff and patient factors including access to ART and patient losses, made us conclude that the lack of clinical staff is the main limiting factor for ART scale-up, assuming that sufficient drugs and supplies are provided by donors. We created a simple formula to estimate the number of patients on ART based on availability and productivity of clinical staff, time needed to initiate vs maintain a patient on ART and patient losses using five different scenarios with varying levels of these parameters. FINDINGS: Our scenario assuming medium productivity (40% higher than that observed in 2002) and medium loss of patients (20% in addition to 15% first-year mortality) coincides with the actual reported number of patients initiated on ART up to 2008, but is considerably below the national CTP target of 90% coverage for 2009, corresponding to 420,000 on ART and 710,000 life-years saved (LY's). Our analysis suggests that a coverage of 40% or 175,000 on treatment and 350,000 LY's saved is more achievable. CONCLUSION: A comparison of our scenario estimations and actual output 2006-2008 indicates that a simple user-friendly dynamic model can estimate the capacity for ART scale-up in resource-poor settings based on identification of a limiting staff factor and information on availability of this staff and patient losses. Thus, it is possible to set more achievable targets

Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa

Groundwater in sub-Saharan Africa supports livelihoods and poverty alleviation1,2, maintains vital ecosystems, and strongly influences terrestrial water and energy budgets. Yet the hydrological processes that govern groundwater recharge and sustainability—and their sensitivity to climatic variability—are poorly constrained4. Given the absence of firm observational constraints, it remains to be seen whether model-based projections of decreased water resources in dry parts of the region4 are justified. Here we show, through analysis of multidecadal groundwater hydrographs across sub-Saharan Africa, that levels of aridity dictate the predominant recharge processes, whereas local hydrogeology influences the type and sensitivity of precipitation–recharge relationships. Recharge in some humid locations varies by as little as five per cent (by coefficient of variation) across a wide range of annual precipitation values. Other regions, by contrast, show roughly linear precipitation–recharge relationships, with precipitation thresholds (of roughly ten millimetres or less per day) governing the initiation of recharge. These thresholds tend to rise as aridity increases, and recharge in drylands is more episodic and increasingly dominated by focused recharge through losses from ephemeral overland flows. Extreme annual recharge is commonly associated with intense rainfall and flooding events, themselves often driven by large-scale climate controls. Intense precipitation, even during years of lower overall precipitation, produces some of the largest years of recharge in some dry subtropical locations. Our results therefore challenge the ‘high certainty’ consensus regarding decreasing water resources in such regions of sub-Saharan Africa. The potential resilience of groundwater to climate variability in many areas that is revealed by these precipitation–recharge relationships is essential for informing reliable predictions of climate-change impacts and adaptation strategies