Evaluating the U.S. Global Health Policy Initiative

In understanding that the efforts made in improving global health affects the health of U.S. citizens, a policy analysis of President Barak Obama's Global Health Initiative was conducted. Using materials gathered from experts in the field of health and their findings and recommendations, paired with the current policies of other G8 countries that pledged to support the efforts of improving global health, the analysis was conducted using four specifically defined criteria. The set criteria determine the appropriateness, responsiveness, effectiveness and equity of Obama's GHI in comparison to other G8 country health policies and overall global health priorities. G8 countries without a specific global health policy, or with a policy that was not in English were excluded from this study and Switzerland, headquarters of the World Health Organization, was added due to its membership in the OECD, and the fact that it has a specific foreign health policy. In evaluating the U.S. Global Health Initiative it is clear that in terms of implementing foreign policy specific to health, the United States is on the forefront alongside the United Kingdom and Switzerland. Other G8 Countries have pledged monies and in order to Millennium Development Health Goals by 2015. The U.S. Global Health Policy does not address issues necessary to meet Millennium Development Goals in Health. Instead the Global Health Initiative is focused narrowly on Fighting and rolling back the HIV/Aids Epidemic based on President Bush's PEPFAR policy. Policy recommendations for a more effective and efficient Global Health Initiative include building upon the PEPFAR policy foundation in order to strengthen health systems worldwide, allowing individuals and communities to combat unnecessary death and disease through research, education, and other preventative methods.^

Evaluation of the Hosmer -Lemeshow global goodness -of-fit statistic for mixed variable logistic regression models

The performance of the Hosmer-Lemeshow global goodness-of-fit statistic for logistic regression models was explored in a wide variety of conditions not previously fully investigated. Computer simulations, each consisting of 500 regression models, were run to assess the statistic in 23 different situations. The items which varied among the situations included the number of observations used in each regression, the number of covariates, the degree of dependence among the covariates, the combinations of continuous and discrete variables, and the generation of the values of the dependent variable for model fit or lack of fit.^ The study found that the $\rm\ C$g* statistic was adequate in tests of significance for most situations. However, when testing data which deviate from a logistic model, the statistic has low power to detect such deviation. Although grouping of the estimated probabilities into quantiles from 8 to 30 was studied, the deciles of risk approach was generally sufficient. Subdividing the estimated probabilities into more than 10 quantiles when there are many covariates in the model is not necessary, despite theoretical reasons which suggest otherwise. Because it does not follow a X$\sp2$ distribution, the statistic is not recommended for use in models containing only categorical variables with a limited number of covariate patterns.^ The statistic performed adequately when there were at least 10 observations per quantile. Large numbers of observations per quantile did not lead to incorrect conclusions that the model did not fit the data when it actually did. However, the statistic failed to detect lack of fit when it existed and should be supplemented with further tests for the influence of individual observations. Careful examination of the parameter estimates is also essential since the statistic did not perform as desired when there was moderate to severe collinearity among covariates.^ Two methods studied for handling tied values of the estimated probabilities made only a slight difference in conclusions about model fit. Neither method split observations with identical probabilities into different quantiles. Approaches which create equal size groups by separating ties should be avoided. ^