Restitution of gut microbiota in Ugandan children administered with probiotics (Lactobacillus rhamnosus GG and Bifidobacterium animalis subsp. lactis BB-12) during treatment for severe acute malnutrition.

Severe acute malnutrition (SAM) is a major challenge in low-income countries and gut microbiota (GM) dysbiosis may play a role in its etiology. Here, we determined the GM evolution during rehabilitation from SAM and the impact of probiotics (Lactobacillus rhamnosus GG and Bifidobacterium animalis subsp. lactis BB-12) supplementation. The GM (16S rRNA gene amplicon sequencing) of children admitted to hospital with SAM showed distinct composition over admission (e.g. Klebsiella spp., and Enterobacteriaceae spp.), discharge (e.g. Clostridiaceae spp., Veilonella dispar) and follow-up (e.g. Lactobacillus ruminis, Blautia spp., Faecalibacterium prausnitzii), reaching similar ß- and α-diversity as healthy individuals. Children with diarrhea had reduced distribution of Bacteroidaceae, Lachnospiraceae, increased Enterobacteriaceae and Moraxellaceae, and lower α-diversity. Children suffering from edematous SAM had diminished proportion of Prevotellaceae, Lachnospiraceae, Ruminoccaceae and a higher α-diversity when compared to non-edematous SAM. Supplementation of probiotics did not influence ß-diversity upon discharge or follow-up, but it increased (p < .05) the number of observed species [SE: > 4.5]. Children where the probiotic species were detected had lower cumulative incidence (p < .001) of diarrhea during the follow-up period compared to children receiving placebo and children receiving probiotics, but where the probiotics were not detected. The GM of children with non-edematous and edematous SAM differ in composition, which might have implications for future GM targeted treatments. Probiotics treatment reduced the cumulative incidence of diarrhea during the outpatient phase, with the strongest effect in children where the administered probiotics could be detected in the GM.

Using cross-sectional surveys to estimate the number of severely malnourished children needing to be enrolled in specific treatment programmes.

OBJECTIVE: When planning severe acute malnutrition (SAM) treatment services, estimates of the number of children requiring treatment are needed. Prevalence surveys, used with population estimates, can directly estimate the number of prevalent cases but not the number of subsequent incident cases. Health managers often use a prevalence-to-incidence conversion factor (J) derived from two African cohort studies to estimate incidence and add the expected number of incident cases to prevalent cases to estimate expected SAM caseload for a given period. The present study aimed to estimate J empirically in different contexts. DESIGN: Observational study, with J estimated by correlating expected numbers of children to be treated, based on prevalence surveys, population estimates and assumed coverage, with the observed numbers of SAM patients treated. SETTING: Survey and programme data from six African and Asian countries. SUBJECTS: Twenty-four data sets including prevalence surveys and programme admissions data for 5 months following the survey. RESULTS: A statistically significant relationship between the number of SAM cases admitted to SAM treatment services and the estimated burden of SAM from prevalence surveys was found. Estimate for the slope (intercept forced to be zero) was 2·17 (95 % CI 1·33, 3·79). Estimates for the prevalence-to-incidence conversion factor J varied from 2·81 to 11·21, assuming programme coverage of 100 % and 38 %, respectively. CONCLUSIONS: Estimation of expected caseload from prevalence may require revision of the currently used prevalence-to-incidence conversion factor J of 1·6. Appropriate values for J may vary between different locations.