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The study investigated the determinants of national savings by employing the Johansen cointegration technique and error correction model to examine the short run and long run dynamics of the system using time-series data for Ghana over the 1975-2008 period. The study found all the variables to be integrated of order one and the existence of co integration indicated a valid long run economic relationship among the determinants of national saving in Ghana. The empirical results established that in the long run, income and terms of trade have a positive and significant impact on savings while dependency ratio, political instability and the real interest rate have a negative impact on savings. In the short run however, only terms of trade positively affects savings. The other variables namely dependency ratio, political instability, financial deepening, income and interest rate have an insignificant impact on savings. The error correction term has a coefficient of -0.830376 which shows that there will be about 83.04 percent speed of adjustment toward long run equilibrium when there is any imbalance in the short run.Keywords: National Savings, Co-integration; Life Cycle Hypothesis, Error Correction Model; Ghana

OBJECTIVES/SPECIFIC AIMS: We hypothesize that VL testing varies by geographic sub-region, country, age, gender, mode of transmission, year of diagnosis, and country of origin; and also that a higher prevalence of VL testing may be associated with higher prevalence of population-level VL suppression. Our primary aim is to determine country- and regional-level factors that are associated with viral load testing amongst HIV patients. Our secondary aim is to explore the association between prevalence of viral load testing and viral load suppression at the population level. METHODS/STUDY POPULATION: This is a retrospective analysis of de-identified individual-level data reported to the European Surveillance System (TESSy). The TESSy is a database of communicable diseases (including HIV) for the ECDC and WHO European Regional Office. It captures data from 31 European Union/European Economic Area (EU/EEA) countries and 23 non-EU/EEA countries. Stored data is from year 2000. TESSy is used for data analysis and production of outputs for public health action. The patient cohort include adults older 18 years, whose last clinic attendance was reported in 2014 or later, or whose viral load test was reported in the year of the visit or the year before the year of their last reported clinic attendance. Patient demographic data include age, sex, mode of transmission, country of origin (migrants), country of diagnosis, geographic region, last clinic attendance, viral load and therapy status. Geographic region will be categorized into East, West and Centre as per WHO guidelines. Countries will be categorized and analyzed according to their European Union (EU)-, European Economic Area (EEA)- and income (GDP)-status, using current World Bank and International Monetary Fund (IMF) guidelines. All statistical analysis will be performed in R-Studio and R i386 3.0.2. Missing data will be characterized in terms of quantity (how much is missing) and pattern (random versus non-random) and impact on covariates to be tested. Multiple data ...