Target carbon's costs, policy designs, and developing countries

Background. In nonhealthcare settings, widespread screening for acute human immunodeficiency virus (HIV) infection (AHI) is limited by cost and decision algorithms to better prioritize use of resources. Comparative cost analyses for available strategies are lacking.Methods. To determine cost-effectiveness of community-based testing strategies, we evaluated annual costs of 3 algorithms that detect AHI based on HIV nucleic acid amplification testing (EarlyTest algorithm) or on HIV p24 antigen (Ag) detection via Architect (Architect algorithm) or Determine (Determine algorithm) as well as 1 algorithm that relies on HIV antibody testing alone (Antibody algorithm). The cost model used data on men who have sex with men (MSM) undergoing community-based AHI screening in San Diego, California. Incremental cost-effectiveness ratios (ICERs) per diagnosis of AHI were calculated for programs with HIV prevalence rates between 0.1% and 2.9%.Results. Among MSM in San Diego, EarlyTest was cost-savings (ie, ICERs per AHI diagnosis less than $13.000) when compared with the 3 other algorithms. Cost analyses relative to regional HIV prevalence showed that EarlyTest was cost-effective (ie, ICERs less than $69.547) for similar populations of MSM with an HIV prevalence rate >0.4%; Architect was the second best alternative for HIV prevalence rates >0.6%.Conclusions. Identification of AHI by the dual EarlyTest screening algorithm is likely to be cost-effective not only among at-risk MSM in San Diego but also among similar populations of MSM with HIV prevalence rates >0.4%.

Objectives: Traditional economic evaluations for most health technology assessments (HTAs) have previously not included environmental outcomes. With the growing interest in reducing the environmental impact of human activities, the need to consider how to include environmental outcomes into HTAs has increased. We present a simple method of doing so. Methods: We adapted an existing clinical-economic model to include environmental outcomes (carbon dioxide [CO 2 ] emissions) to predict the consequences of adding insulin to an oral antidiabetic (OAD) regimen for patients with type 2 diabetes mellitus (T2DM) over 30 years, from the United Kingdom payer perspective. Epidemiological, efficacy, healthcare costs, utility, and carbon emissions data were derived from published literature. A scenario analysis was performed to explore the impact of parameter uncertainty.Results: The addition of insulin to an OAD regimen increases costs by 2,668 British pounds per patient and is associated with 0.36 additional quality-adjusted life-years per patient. The insulin-OAD combination regimen generates more treatment and disease management-related CO 2 emissions per patient (1,686 kg) than the OAD-only regimen (310 kg), but generates fewer emissions associated with treating complications (3,019 kg versus 3,337 kg). Overall, adding insulin to OAD therapy generates an extra 1,057 kg of CO 2 emissions per patient over 30 years. Conclusions: The model offers a simple approach for incorporating environmental outcomes into health economic analyses, to support a decision-maker's objective of reducing the environmental impact of health care. Further work is required to improve the accuracy of the approach; in particular, the generation of resource-specific environmental impacts.

Climb atop shoulders and wait for funerals. That, suggested Newton and then Planck, is how science advances (more or less). We've come far since then, but many notions about how people and practices, policies, and resources influence the course of science are still more rooted in traditions and intuitions than in evidence. We can and must do better, lest we resign ourselves to “intuition-based policy” when making decisions and investments aimed at driving scientific progress. Science invited experts to highlight key aspects of the scientific enterprise that are steadily yielding to empirical investigation—and to explain how Newton and Planck got it right (and Einstein got it wrong). — Brad Wible