Objective The aim was to describe how selected health research funding agencies active in low-and middle-income countries promote the translation of their funded research into policy and practice. Methods We conducted inductive analysis of semi-structured interviews with key informants from a purposive sample of 23 national and international funding agencies that fund health research in Brazil, Colombia, India, the Philippines, South Africa and Thailand. We also surveyed web sites. Findings We found a commitment to knowledge translation in the mandate of 18 of 23 agencies. However, there was a lack of common terminology. Most of the activities were traditional efforts to disseminate to a broad audience, for example using web sites and publications. In addition, more than half (13 of 23) of the agencies encouraged linkage/exchange between researchers and potential users, and 6 of 23 agencies described "pull" activities to generate interest in research from decision-makers. One-third (9 of 23) of funding agencies described a mandate to enhance health equity through improving knowledge translation. Only 3 of 23 agencies were able to describe evaluation of knowledge translation activities. Furthermore, we found national funding agencies made greater knowledge translation efforts when compared to international agencies. Conclusion Funding agencies are engaged in a wide range of creative knowledge translation activities. They might consider their role as knowledge brokers, with an ability to promote research syntheses and a focus on health equity. There is an urgent need to evaluate the knowledge translation activities of funding agencies.

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BackgroundMucopolysaccharidosis type II, an X-linked recessive disorder is the most common lysosomal storage disease detected among Filipinos. This is a case series involving 23 male Filipino patients confirmed to have Hunter syndrome. The clinical and biochemical characteristics were obtained and mutation testing of the IDS gene was done on the probands and their female relatives.ResultsThe mean age of the patients was 11.28 (SD 4.10) years with an average symptom onset at 1.2 (SD 1.4) years. The mean age at biochemical diagnosis was 8 (SD 3.2) years. The early clinical characteristics were developmental delay, joint stiffness, coarse facies, recurrent respiratory tract infections, abdominal distention and hernia. Majority of the patients had joint contractures, severe intellectual disability, error of refraction, hearing loss and valvular regurgitation on subspecialists’ evaluation. The mean GAG concentration was 506.5 mg (SD 191.3)/grams creatinine while the mean plasma iduronate-2-sulfatase activity was 0.86 (SD 0.79) nmol/mg plasma/4 h. Fourteen (14) mutations were found: 6 missense (42.9%), 4 nonsense (28.6%), 2 frameshift (14.3%), 1 exon skipping at the cDNA level (7.1%), and 1 gross insertion (7.1%). Six (6) novel mutations were observed (43%): p.C422F, p.P86Rfs*44, p.Q121*, p.L209Wfs*4, p.T409R, and c.1461_1462insN[710].ConclusionThe age at diagnosis in this series was much delayed and majority of the patients presented with severe neurologic impairment. The results of the biochemical tests did not contribute to the phenotypic classification of patients. The effects of the mutations were consistent with the severe phenotype seen in the majority of the patients.

In a meta-analysis, a question always arises. Is it worthwhile to combine estimates from studies of different populations using various formulations of an intervention, evaluating outcomes measured differently? Sometimes even study designs differ. Differences are expected in a meta-analysis. These may be negligible, and a pooled estimate of effect can guide the clinical decision. However, when the differences are large, this estimate may mislead. Effect estimates from study to study differ because of real differences (between-study variability) and because of chance (within-study variability). To combine estimates when there is heterogeneity (between-study differences are large) may not be sensible. Two complementary methods may be used to detect heterogeneity: visual inspection of the forest plot and calculating numerical measures of heterogeneity (I 2 and Q). Visual inspection can show effects that are different from the rest. A large I 2 (proportion of overall variability attributed to between-study variation) or a small P-value associated with Q may suggest heterogeneity. Large P-values, however, do not mean the absence of heterogeneity. It is more informative to report the confidence interval of the I 2 . If there is no heterogeneity, a pooled estimate of the true effect may be generated using only within-study variation (fixed-effect model). If there is substantial heterogeneity, reasons should be sought. Subgroup analysis or meta-regression using study-level characteristics may be done. Although more involved and potentially challenging, individual-level data (Individual Participant Data, IPD) may also be used. In the case of unexplained heterogeneity, both within-and between-study variation should be used to generate a pooled estimate (random-effects model). This estimate does not estimate a single true effect but estimates the average of a range of effects of the intervention on populations represented by the studies. If precise enough (narrow confidence interval), this estimate, together with the prediction interval (a measure of uncertainty in the effect one might see in a particular context), can guide clinical and policy decisions.

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