Assessing the Impact of Integrated Community-Based Management of Severe Wasting Programs in Conflict-Stricken South Sudan: A Multi-Dimensional Approach to Scalability of Nutrition Emergency Response Programs
Bibliographic record
Abstract
Community-based management of severe wasting (CMSW) programs have solely focused on exit outcome indicators, often omitting data on nutrition emergency preparedness and scalability. This study aimed to document good practices and generate evidence on the effectiveness and scalability of CMSW programs to guide future nutrition interventions in South Sudan. A total of 69 CMSW program implementation documents and policies were authenticated and retained for analysis, complemented with the analyses of aggregated secondary data obtained over five (2016-2020 for CMSW program performance) to six (wasting prevention) years (2014-2019). Findings suggest a strong and harmonised coordination of CMSW program implementation, facilitated timely and with quality care through an integrated and harmonised multi-agency and multidisciplinary approach. There were challenges to the institutionalisation and ownership of CMSW programs: a weak health system, fragile health budget that relied on external assistance, and limited opportunities for competency-based learning and knowledge transfer. Between 2014 and 2019, the prevalence of wasting fluctuated according to the agricultural cycle and remained above the emergency threshold of 15% during the July to August lean season. However, during the same period, under-five and crude mortality rates (10,000/day) declined respectively from 1.17 (95% confidence interval (CI): 0.91, 1.43) and 1.00 (95% CI: 0.75, 1.25) to 0.57 (95% CI: 0.38, 0.76) and 0.55 (95% CI: 0.39, 0.70). Both indicators remained below the emergency thresholds, hence suggesting that the emergency response was under control. Over a five-year period (2016-2020), a total of 1,105,546 children (52% girls, 48% boys) were admitted to CMSW programs. The five-year pooled performance indicators (mean [standard deviations]) was 86.4 (18.9%) for recovery, 2.1 (7.8%) for deaths, 5.2 (10.3%) for defaulting, 1.7 (5.7%) for non-recovery, 4.6 (13.5%) for medical transfers, 2.2 (4.7%) for relapse, 3.3 (15.0) g/kg/day for weight gain velocity, and 6.7 (3.7) weeks for the length of stay in the program. In conclusion, all key performance indicators, except the weight gain velocity, met or exceeded the Humanitarian Charter and Minimum Standards in Humanitarian Response. Our findings demonstrate the possibility of implementing robust and resilient CMSAM programs in protracted conflict environments, informed by global guidelines and protocols. They also depict challenges to institutionalisation and ownership.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".