Effects of vaccine program against pandemic influenza A(H1N1) virus, United States, 2009-2010.

In April 2009, the United States began a response to the emergence of a pandemic influenza virus strain: A(H1N1)pdm09. Vaccination began in October 2009. By using US surveillance data (April 12, 2009-April 10, 2010) and vaccine coverage estimates (October 3, 2009-April 18, 2010), we estimated that the A(H1N1)pdm09 virus vaccination program prevented 700,000-1,500,000 clinical cases, 4,000-10,000 hospitalizations, and 200-500 deaths. We found that the national health effects were greatly influenced by the timing of vaccine administration and the effectiveness of the vaccine. We estimated that recommendations for priority vaccination of targeted priority groups were not inferior to other vaccination prioritization strategies. These results emphasize the need for relevant surveillance data to facilitate a rapid evaluation of vaccine recommendations and effects.

A Monte Carlo study of the impact of missing data and differential item functioning on theta estimates from two polytomous Rasch family models.

This paper examines the impact of differential item functioning (DIF), missing item values, and different methods for handling missing item values on theta estimates with data simulated from the partial credit model and Andrich's rating scale model. Both Rasch family models are commonly used when obtaining an estimate of a respondent's attitude. The degree of missing data, DIF magnitude, and the percentage of DIF items were varied in MCAR data conditions in which the focal group was 10% of the total population. Four methods for handling missing data were compared: complete-case analysis, mean substitution, hot-decking, and multiple imputation. Bias, RMSE, means, and standard errors of the theta estimates for the focal group were adversely affected by the amount and magnitude of DIF items. RMSE and fidelity coefficients for both the reference and focal group were adversely impacted by the amount of missing data. While all methods of handling missing data performed fairly similarly, multiple imputation and hot-decking showed slightly better performance.

Meta-analytic methods of pooling correlation matrices for structural equation modeling under different patterns of missing data.

Three methods of synthesizing correlations for meta-analytic structural equation modeling (SEM) under different degrees and mechanisms of missingness were compared for the estimation of correlation and SEM parameters and goodness-of-fit indices by using Monte Carlo simulation techniques. A revised generalized least squares (GLS) method for synthesizing correlations, weighted-covariance GLS (W-COV GLS), was compared with univariate weighting with untransformed correlations (univariate r) and univariate weighting with Fisher's z-transformed correlations (univariate z). These 3 methods were crossed with listwise and pairwise deletion. Univariate z and W-COV GLS performed similarly, with W-COV GLS providing slightly better estimation of parameters and more correct model rejection rates. Missing not at random data produced high levels of relative bias in correlation and model parameter estimates and higher incorrect SEM model rejection rates. Pairwise deletion resulted in inflated standard errors for all synthesis methods and higher incorrect rejection rates for the SEM model with univariate weighting procedures.