Developing an HMIS Architecture Framework to Support a National Health Care eHealth Strategy Reform: A Case Study from Morocco.

Abstract-An increasing number of low- and middle-income countries are receiving significant investments to implement health reform strategies featuring a health management information system (HMIS) as a fundamental eHealth intervention. We present the case of Morocco's first step toward the implementation of a national HMIS: the "urbanization" of its health information systems-an information architecture methodology designed to leverage existing capacity while ensuring sustainability of the new HMIS. We report on this process and share lessons learned, applicable to similar countries involved in HMIS interventions, including involving all stakeholders from inception to rollout, encouraging local ownership of the new HMIS, fostering active data usage among users, and leveraging existing personnel rotation policies when developing adoption strategies and facilitating capacity building efforts.

Assessing differential expression in two-color microarrays: a resampling-based empirical Bayes approach.

Microarrays are widely used for examining differential gene expression, identifying single nucleotide polymorphisms, and detecting methylation loci. Multiple testing methods in microarray data analysis aim at controlling both Type I and Type II error rates; however, real microarray data do not always fit their distribution assumptions. Smyth's ubiquitous parametric method, for example, inadequately accommodates violations of normality assumptions, resulting in inflated Type I error rates. The Significance Analysis of Microarrays, another widely used microarray data analysis method, is based on a permutation test and is robust to non-normally distributed data; however, the Significance Analysis of Microarrays method fold change criteria are problematic, and can critically alter the conclusion of a study, as a result of compositional changes of the control data set in the analysis. We propose a novel approach, combining resampling with empirical Bayes methods: the Resampling-based empirical Bayes Methods. This approach not only reduces false discovery rates for non-normally distributed microarray data, but it is also impervious to fold change threshold since no control data set selection is needed. Through simulation studies, sensitivities, specificities, total rejections, and false discovery rates are compared across the Smyth's parametric method, the Significance Analysis of Microarrays, and the Resampling-based empirical Bayes Methods. Differences in false discovery rates controls between each approach are illustrated through a preterm delivery methylation study. The results show that the Resampling-based empirical Bayes Methods offer significantly higher specificity and lower false discovery rates compared to Smyth's parametric method when data are not normally distributed. The Resampling-based empirical Bayes Methods also offers higher statistical power than the Significance Analysis of Microarrays method when the proportion of significantly differentially expressed genes is large for both normally and non-normally distributed data. Finally, the Resampling-based empirical Bayes Methods are generalizable to next generation sequencing RNA-seq data analysis.