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The Central Asian region, which encompasses Kazakhstan, Uzbekistan, Tajikistan, Turkmenistan and Kyrgyzstan, is an interesting geographic region with a rich history dating back to the Silk Road, Mongol conquests and expansion of the Russian Empire. However, from a public health viewpoint, the Central Asian region is under-investigated, and many public health challenges exist, as countries of Central Asia inherited the centralised medical systems practiced in the Soviet Union, and are currently undergoing rapid transitions. A large number of low and middle-income countries around the world, including countries of Central Asia, face a double burden of chronic and infectious disease. This essay focuses on the exploration of the most important public health challenges in the Central Asian region, including limited scientific productivity, the double burden of chronic and infectious disease, the need for healthcare reform and the reduction in care variation. Central Asia has a large number of medical schools, medical centres, and emerging research institutes that can be used to foster a change in medical and public health practice in the region.

IntroductionDespite the significant number of research institutions and rich scientific heritage, published research from Central Asia (Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan, and Turkmenistan) is traditionally underrepresented in international scientific literature. The goal of this paper was to analyze publication patterns in Central Asian countries, and to explore the factors that contributed to the publication productivity in Kazakhstan.MethodsPublication productivity was evaluated using data generated by the SCImago Journal & Country Rank over the period of 1996–2014 for all of the 15 former Soviet Union Republics for all subject categories. Country specific data, including total population, gross domestic product (GDP) per capita, research and development (R&D) expenditure (% of GDP), number of reserchers (per million people), was abstracted from World Bank data. ANOVA and ANCOVA analyses compared the mean number of publications among Central Asian countries. Separate analyses was done for publication patterns in the health sciences. Multiple comparisons were performed using Tukey methodResultsThe analysis of publication productivity showed significant discrepancies in the number of published documents among the Central Asian countries. Kazakhstan demonstrated a significant increase in the number of published documents in the period of 1996–2014, mainly in the areas of natural and multidisciplinary sciences. Our analyses also showed that the number of publications are siginicantly associated with GDP and population size.ConclusionsWe identified large gaps in publication productivity among the Central Asian countries. The association between publication rate with GDP and population size indicates there is a need to adjust for these factors when planning research policy.

The Pipelle is the more cost-effective sampling strategy compared to D&C for EC diagnosis in women with PMB. From the cost-effectiveness perspective, the higher sampling failure rate of Pipelle should not be regarded as a limitation in its clinical application.

Background: COVID-19 pandemic has presented extreme challenges to developing countries across the world. Post-Soviet states are facing unique challenges due to their developing healthcare systems and unstable economy. The aim of this paper was to provide estimates for current development COVID-19 pandemic in the Post-Soviet states and forecast potential best and worst scenarios for spread of this deadly infection. Methods:The data on confirmed cases and deaths were extracted from official governmental sources for a period from beginning of outbreak dates for each country until April 18, 2020. A modified SEIR (Susceptible-Exposed-Infected-Recovered) modelling was used to plot the parameters of epidemic in 10 post-Soviet states and forecast the number of cases over a period of 10, 30 and 60 days. We also estimated the numbers of cases based on the optimal measures (best scenario) and suboptimal measures (worst scenarios) of potential spread of COVID-19 in these countries.