GBD 2015 and HIV estimates from the Optima model

Progress towards the 2020 fast track HIV/AIDS reduction targets across ages in Ethiopia as compared to neighboring countries using global burden of diseases 2017 data

Background

Sustainable Development Goal (SDG) 3.3, targets to eliminate HIV from being a public health threat by 2030. For better tracking of this target interim Fast Track milestones for 2020 and composite complementary measures have been indicated. This study measured the Fast Track progress in the epidemiology of HIV/AIDS in Ethiopia across ages compared to neighboring countries.

Methods

The National Data Management Center for health’s research team at the Ethiopian Public Health Institute has analyzed the Global Burden of Disease (GBD) 2017 secondary data for the year 2010 to 2017 for Ethiopia and its neighbors. GBD 2017 data sources were census, demographic and a health survey, prevention of mother-to-child HIV transmission, antiretroviral treatment programs, sentinel surveillance, and UNAIDS reports. Age-standardized and age-specific HIV/AIDS incidence, prevalence, mortality, Disability-Adjusted Life Years (DALYs), incidence:mortality ratio and incidence:prevalence ratio were calculated with corresponding 95% confidence intervals.

Results

Ethiopia and neighboring countries recorded slow progress in reducing new HIV infection since 2010. Only Uganda would achieve the 75% target by 2020. Ethiopia, Tanzania, and Uganda already achieved the 75% mortality reduction target set for 2020. The incidence: prevalence ratio for Ethiopia, Rwanda, and Uganda were < 0.03, indicating the countries were on track to end HIV by 2030. Ethiopia had an incidence: mortality ratio < 1 due to high mortality; while Kenya, Rwanda, Tanzania and Uganda had a ratio of > 1 due to high incidence. The HIV incidence rate in Ethiopia was dropped by 76% among under 5 children in 2017 compared to 2010 and the country would likely to attain the 2020 national target, but far behind achieving the target among the 15–49 age group.

Conclusions

Ethiopia and neighboring countries have made remarkable progress towards achieving the 75% HIV/AIDS mortality reduction target by 2020, although they progressed poorly in reducing HIV incidence. By recording an incidence:prevalence ratio benchmark of less than 0.03, Ethiopia, Rwanda, and Uganda are well heading towards epidemic control. Nonetheless, the high HIV/AIDS mortality rate in Ethiopia for its incidence requires innovative strategies to reach out undiagnosed cases and to build institutional capacity for generating strong evidence to ensure sustainable epidemic control.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10269-y.

Harm reduction programs and policy in Australia: barriers and enablers to effective implementation

Abstract. Background: Harm reduction is an integral component of Australia’s overall national drug policy. Harm reduction policy and interventions can be applied to any legal or illegal drug to mitigate harm without necessarily reducing use, but harm reduction is traditionally conceptualised in relation to injecting drug use. Early and comprehensive adoption of many innovative harm reduction interventions has meant that Australia has had significant success in reducing a number of drug related harms, avoided disease epidemics experienced in other countries, and established programs and practices that are of international renown. However, these gains were not easily established, nor necessarily permanent. Aim: In this paper we explore the past and present harm reduction policy and practice contexts that normalised and facilitated harm reduction as a public health response, as well as those converse contexts currently creating opposition to additional or expanded interventions. Importantly, this paper discusses the intersection between various interventions, such as needle and syringe distribution and drug treatment programs. Finally, we detail some of the practical lessons that have been learned via the Australian experience, with the hope that these lessons will assist to inform and improve international harm reduction implementation.

Investigation of the Proportion of Diagnosed People Living with HIV/AIDS among Foreign Residents in Japan

Foreign residents represent an increasing proportion of newly diagnosed human immunodeficiency virus (HIV) infections and acquired immunodeficiency syndrome (AIDS) cases in Japan, though scant research has addressed this. This study aimed to estimate the diagnosed proportion of people living with HIV/AIDS (PLWHA) among foreign residents in Japan, covering 1990–2017 and stratifying by geographic region of the country of origin. A balance equation model was employed to statistically estimate the diagnosed proportion as a single parameter. This used published estimates of HIV incidence and prevalence, population size, visit duration, travel volume, as well as surveillance data on HIV/AIDS in Japan. The proportion varied widely by region: People from Western Europe, East Asia and the Pacific, Australia and New Zealand, and North America were underdiagnosed, while those from sub-Saharan Africa, South and South-East Asia, and Latin America were more frequently diagnosed. Overall, the diagnosed proportion of PLWHA among foreign residents in Japan has increased, but the latest estimate in 2017 was as low as 55.3%; lower than the estimate among Japanese on the order of 80% and far below the quoted goal of 90%. This finding indicates a critical need to investigate the underlying mechanisms, including disparate access to HIV testing.

Optimization by Adaptive Stochastic Descent

When standard optimization methods fail to find a satisfactory solution for a parameter fitting problem, a tempting recourse is to adjust parameters manually. While tedious, this approach can be surprisingly powerful in terms of achieving optimal or near-optimal solutions. This paper outlines an optimization algorithm, Adaptive Stochastic Descent (ASD), that has been designed to replicate the essential aspects of manual parameter fitting in an automated way. Specifically, ASD uses simple principles to form probabilistic assumptions about (a) which parameters have the greatest effect on the objective function, and (b) optimal step sizes for each parameter. We show that for a certain class of optimization problems (namely, those with a moderate to large number of scalar parameter dimensions, especially if some dimensions are more important than others), ASD is capable of minimizing the objective function with far fewer function evaluations than classic optimization methods, such as the Nelder-Mead nonlinear simplex, Levenberg-Marquardt gradient descent, simulated annealing, and genetic algorithms. As a case study, we show that ASD outperforms standard algorithms when used to determine how resources should be allocated in order to minimize new HIV infections in Swaziland.