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

Improving estimates of the burden of severe wasting: analysis of secondary prevalence and incidence data from 352 sites

Introduction

Estimates of incident cases of severe wasting among young children are not available for most settings but are needed for optimal planning of treatment programmes and burden estimation. To improve programme planning, global guidance recommends a single ‘incidence correction factor’ of 1.6 be applied to available prevalence estimates to account for incident cases. This study aimed to update estimates of the incidence correction factor to improve programme planning and inform the approach to burden estimation for severe wasting.

Methods

A global call was issued for secondary data from severe wasting treatment programmes including prevalence, population size, programme admission and programme coverage through a UNICEF-led effort. Site-specific incidence correction factors were calculated as the number of incident cases (annual programme admissions/programme coverage) divided by the number of prevalent cases (prevalence*population size). Estimates were aggregated by country, region and overall using inverse-variance weighted random-effects meta-analysis.

Results

We estimated incidence correction factors from 352 sites in 20 countries. Estimates aggregated by country ranged from 1.3 (Nigeria) to 30.1 (Burundi). Excluding implausible values, the overall incidence correction factor was 3.6 (95% CI 3.4 to 3.9).

Conclusion

Our results suggest that incidence correction factors vary between sites and that the burden of severe wasting will often be underestimated using the currently recommended incidence correction factor of 1.6. Application of updated incidence correction factors represents a simple way to improve programme planning when incidence data are not available and could inform the approach to burden estimation.

Incidence Correction Factors for Moderate and Severe Acute Child Malnutrition From 2 Longitudinal Cohorts in Mali and Burkina Faso

Child acute malnutrition (AM) is an important cause of child mortality. Accurately estimating its burden requires cumulative incidence data from longitudinal studies, which are rarely available in low-income settings. In the absence of such data, the AM burden is approximated using prevalence estimates from cross-sectional surveys and the incidence correction factor $K$, obtained from the few available cohorts that measured AM. We estimated $K$ factors for severe acute malnutrition (SAM) and moderate acute malnutrition (MAM) from AM incidence and prevalence using representative cross-sectional baseline and longitudinal data from 2 cluster-randomized controlled trials (Innovative Approaches for the Prevention of Childhood Malnutrition-PROMIS) conducted between 2014 and 2017 in Burkina Faso and Mali. We compared K estimates using complete (weight-for-length z score, mid-upper arm circumference (MUAC), and edema) and partial (MUAC, edema) definitions of SAM and MAM. $K$ estimates for SAM were 9.4 and 5.7 in Burkina Faso and in Mali, respectively; K estimates for MAM were 4.7 in Burkina Faso and 5.1 in Mali. The MUAC and edema-based definition of AM did not lead to different $K$ estimates. Our results suggest that $K$ can be reliably estimated when only MUAC and edema-based data are available. Additional studies, however, are required to confirm this finding in different settings.

Acute malnutrition recovery energy requirements based on mid-upper arm circumference: Secondary analysis of feeding program data from 5 countries, Combined Protocol for Acute Malnutrition Study (ComPAS) Stage 1

Background

Severe and moderate acute malnutrition (SAM and MAM) are currently treated with different food products in separate treatment programs. The development of a unified and simplified treatment protocol using a single food product aims to increase treatment program efficiency and effectiveness. This study, the first stage of the ComPAS trial, sought to assess rate of growth and energy requirements among children recovering from acute malnutrition in order to design a simplified, MUAC-based dosage protocol.

Methods

We obtained secondary data from patient cards of children aged 6–59 months recovering from SAM in outpatient therapeutic feeding programs (TFPs) and from MAM in supplementary feeding programs (SFPs) in five countries in Africa and Asia. We used local polynomial smoothing to assess changes in MUAC and proportional weight gain between clinic visits and assessed their normalized differences for a non-zero linear trend. We estimated energy needs to meet or exceed the growth observed in 95% of visits.

Results

This analysis used data from 5518 patients representing 33942 visits. Growth trends in MUAC and proportional weight gain were not significantly different, each lower at higher MUAC values: MUAC growth averaged 2mm/week at lower MUACs (100 to <110mm) and 1mm/week at higher MUACs (120mm to <125mm); and proportional weight gain declined from 3.9g/kg/day to 2.4g/kg/day across the same MUAC values. In 95% of visits by children with a MUAC 100mm to <125mm who were successfully treated, energy needs could be met or exceeded with 1,000 kilocalories a day.

Conclusion

Two 92g sachets of Ready-to-Use Therapeutic Food (RUTF) (1,000kcal total) is proposed to meet the estimated total energy requirements of children with a MUAC 100mm to <115mm, and one 92g sachet of RUTF (500kcal) is proposed to meet half the energy requirements of children with a MUAC of 115 to <125mm. A simplified, combined protocol may enable a more holistic continuum of care, potentially contributing to increased coverage for children suffering from acute malnutrition.

Disability-adjusted life years for severe acute malnutrition: implications of alternative model specifications

OBJECTIVE

Reducing the burden of childhood severe acute malnutrition (SAM) is key to improving global child health outcomes. Assessing cost-effectiveness of nutrition interventions remains an important evidence gap. Disability-adjusted life years (DALYs) are a common indicator used in cost-effectiveness analyses. DALYs were established by the Global Burden of Disease (GBD) study. Recent iterations of the GBD have changed the methods used to calculate DALYs by dropping age-weighting and discounting (AD) and updating disability weights (DW). Cost-effectiveness analyses may use either local or international standard life expectancies (LE). Changes in model specifications for calculating DALYs may have implications for cost-effectiveness analyses using DALYs, interpreting historical DALY estimates, and related resource allocation decisions. The present study aimed to quantify the magnitude of change in estimates of DALYs attributable to SAM given recent methodological changes.

DESIGN

From secondary data analysis, using parameter values from routine programme monitoring data for two SAM treatment programmes and published literature, eight calculation models were created to estimate DALYs with and without AD, using different sets of DW, and local v. standard LE.

RESULTS

Different DW had a marginal effect on DALY estimates. Different LE had a small effect when AD was used, but a large effect when AD was not used.

CONCLUSIONS

DALY estimates are sensitive to the model used. This complicates comparisons between studies using different models and needs to be accounted for in decision making. It seems sensible for analyses to report results using models with and without AD and using local and standard LE.

Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from Nigeria

Background

The burden of severe acute malnutrition (SAM) is estimated using unadjusted prevalence estimates. SAM is an acute condition and many children with SAM will either recover or die within a few weeks. Estimating SAM burden using unadjusted prevalence estimates results in significant underestimation. This has a negative impact on allocation of resources for the prevention and treatment of SAM. A simple method for adjusting prevalence estimates intended to improve the accuracy of burden estimates and caseload predictions has been proposed. This method employs an incidence correction factor. Application of this method using the globally recommended incidence correction factor has led to programs underestimating burden and caseload in some settings.

Methods

A method for estimating a locally appropriate incidence correction factor from prevalence, population size, program caseload, and program coverage was developed and tested using data from the Nigerian national SAM treatment program.

Results

Applying the developed method resulted in errors in caseload prediction of about 10%. This is a considerable improvement upon the current method, which resulted in a 79.5% underestimate. Methods for improving the precision of estimates are proposed.

Conclusions

It is possible to considerably improve predictions of caseload by applying a simple model to data that are readily available to program managers. This implies that more accurate estimates of burden may also be made using the same methods and data.