Human resources: the Cinderella of health sector reform in Latin America

Human resources are the most important assets of any health system, and health workforce problems have for decades limited the efficiency and quality of Latin America health systems. World Bank-led reforms aimed at increasing equity, efficiency, quality of care and user satisfaction did not attempt to resolve the human resources problems that had been identified in multiple health sector assessments. However, the two most important reform policies – decentralization and privatization – have had a negative impact on the conditions of employment and prompted opposition from organized professionals and unions. In several countries of the region, the workforce became the most important obstacle to successful reform. This article is based on fieldwork and a review of the literature. It discusses the reasons that led health workers to oppose reform; the institutional and legal constraints to implementing reform as originally designed; the mismatch between the types of personnel needed for reform and the availability of professionals; the deficiencies of the reform implementation process; and the regulatory weaknesses of the region. The discussion presents workforce strategies that the reforms could have included to achieve the intended goals, and the need to take into account the values and political realities of the countries. The authors suggest that autochthonous solutions are more likely to succeed than solutions imported from the outside

Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs

<p>Abstract</p> <p>Background</p> <p>We explored the imputation performance of the program IMPUTE in an admixed sample from Mexico City. The following issues were evaluated: (a) the impact of different reference panels (HapMap vs. 1000 Genomes) on imputation; (b) potential differences in imputation performance between single-step vs. two-step (phasing and imputation) approaches; (c) the effect of different INFO score thresholds on imputation performance and (d) imputation performance in common vs. rare markers.</p> <p>Methods</p> <p>The sample from Mexico City comprised 1,310 individuals genotyped with the Affymetrix 5.0 array. We randomly masked 5% of the markers directly genotyped on chromosome 12 (n = 1,046) and compared the imputed genotypes with the microarray genotype calls. Imputation was carried out with the program IMPUTE. The concordance rates between the imputed and observed genotypes were used as a measure of imputation accuracy and the proportion of non-missing genotypes as a measure of imputation efficacy.</p> <p>Results</p> <p>The single-step imputation approach produced slightly higher concordance rates than the two-step strategy (99.1% vs. 98.4% when using the HapMap phase II combined panel), but at the expense of a lower proportion of non-missing genotypes (85.5% vs. 90.1%). The 1,000 Genomes reference sample produced similar concordance rates to the HapMap phase II panel (98.4% for both datasets, using the two-step strategy). However, the 1000 Genomes reference sample increased substantially the proportion of non-missing genotypes (94.7% vs. 90.1%). Rare variants (<1%) had lower imputation accuracy and efficacy than common markers.</p> <p>Conclusions</p> <p>The program IMPUTE had an excellent imputation performance for common alleles in an admixed sample from Mexico City, which has primarily Native American (62%) and European (33%) contributions. Genotype concordances were higher than 98.4% using all the imputation strategies, in spite of the fact that no Native American samples are present in the HapMap and 1000 Genomes reference panels. The best balance of imputation accuracy and efficiency was obtained with the 1,000 Genomes panel. Rare variants were not captured effectively by any of the available panels, emphasizing the need to be cautious in the interpretation of association results for imputed rare variants.</p