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Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from Nigeria
- Assaye Bulti^{1}Email authorView ORCID ID profile,
- André Briend^{2, 3},
- Nancy M. Dale^{2},
- Arjan De Wagt^{1},
- Faraja Chiwile^{1},
- Stanley Chitekwe^{4},
- Chris Isokpunwu^{5} and
- Mark Myatt^{6}
https://doi.org/10.1186/s13690-017-0234-4
© The Author(s). 2017
Received: 2 June 2017
Accepted: 28 September 2017
Published: 9 November 2017
Abstract
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.
Keywords
Background
A child with severe acute malnutrition (SAM) has a high risk of near term mortality [1, 2]. It has been estimated that SAM affected more than 16 million children globally in 2016 [3]. This figure is based on prevalence estimates from cross-sectional surveys. SAM is an acute condition and many children with SAM will either recover or die within a few weeks. Estimating the number of SAM cases present in a population over a given period of time, the “SAM burden”, using unadjusted prevalence estimates is likely, therefore, to miss many new (incident) cases and significantly underestimate the SAM burden [4]. A recent estimate of the annual global SAM burden that attempts to account for incident cases suggests that 110 million cases per year might be a more accurate estimate [5]. Poor estimates of SAM burden are a problem for program managers at all levels. Underestimation has a negative impact on the prioritization of resource allocation for the prevention and treatment of SAM both globally and locally [6].
Burden is the sum of prevalent cases at the start of a period and incident cases that arise during that period. The number of prevalent cases in a population at a given point in time can be estimated using a combination of a prevalence estimate from a cross-sectional survey and population data. This information is usually already available to program managers. Incidence is more complicated and more expensive to estimate.
The simple relationship between prevalence, incidence, and duration of illness makes it possible to create a simple mathematical model that allows the estimation of burden using prevalence and population estimates together with other data (e.g. program coverage and program caseloads) that will usually be available to program managers.
All of the terms in this estimator are subject to uncertainty.
Coverage is typically estimated with a precision of about ± 10% on a 50% estimate [12]. This is a 40% relative precision.
Useful accuracy of population estimates can be achieved by correcting census data to account for population growth and migration. It can often be assumed that the population is estimated with little or no error. This may not, however, be the case in emergencies in which there is considerable and ongoing population movement and / or high levels of mortality.
Caseload (L) is a simple count of program admissions. This data is collected and reported on a routine basis and can usually be assumed to be measured with little or no error.
Values of incidence correction factors (K) found in recent studies^{a}
Country | Year(s) | K ^{b} | SAM case definition(s)^{c} | W/H Reference^{c} | Data Source(s) | Method | Source |
---|---|---|---|---|---|---|---|
Niger | 2010–2013 | 4.30–9.50 | W/H < −3 z-scores or MUAC <115 mm or bilateral pitting edema | WGS | Surveillance system (weekly) Routine program data (weekly) Routine program data (monthly) | Simple mathematical models | Deconinck et al., 2016 [14] |
Niger | 2006–2007 | 5.37–11.78 | W/H < −3 z-scores or MUAC <115 mm or bilateral pitting edema | WGS | Community cohort (monthly) | Compartmental model to estimate mean duration of SAM episodes | Isanaka et al., 2011 [6] |
Mali | 2010–2013 | 2.10–2.50 | W/H < −3 z-scores or MUAC <115 mm or bilateral pitting edema | WGS | Community cohort (quarterly) Surveys (occasional) | Simple mathematical models | Isanaka et al., 2016 [15] |
Niger | 2010–2011 | 5.00–8.10 | W/H < −3 z-scores or bilateral pitting edema | WGS | Community cohort (monthly) Surveys (monthly) | Simple mathematical models | |
Burkina Faso | 2009–2010 | 7.30–17.00 | MUAC <110 mm (prevalence) MUAC <120 mm (incidence) | WGS | Surveys (annual) Routine program data (monthly) | Simple mathematical models | |
Various^{d} | 2005–2009 | 11.21 | W/H < 70% of median | NCHS | Surveys Caseloads for 5 months after survey | Linear regression | Dale et al., 2017 [16] |
Data from the Nigerian Community-based Management of Acute Malnutrition (CMAM) program from 2014 and 2015 are presented in this article. This program started operations in 2009 and has treated between 300 thousand and 500 thousand SAM cases each year. During the course of implementation it was recognized that the use of K = 1.6 had led to considerable underestimation of SAM burden and program caseload. Given the public health and security situation in Nigeria it is anticipated that the Nigerian CMAM program will run for many years and accurate estimates of expected caseloads will be required to secure adequate continued funding.
This article presents a method to adjust or calibrate the value of K using the population of the program area, the number of program admissions, estimates of program coverage, and estimates of the prevalence of SAM in order to provide more accurate estimates of burden and expected caseload during program implementation. The method is illustrated using data from the Nigerian CMAM program. The revised estimate of K may also be useful to predict SAM burden and caseload from prevalence surveys in similar settings.
Methods
A suitable value for K can be found by substituting known values for L, C, N, and P with L being the observed program caseload (i.e. the number of admissions).
The method outlined here assumes that both population (N) and caseload (L) are measured with little or no error although the method can be easily extended to accommodate uncertainty in these terms. The principal sources of uncertainty in this analysis are, therefore, prevalence (P) and coverage (C). This can lead to considerable uncertainty in their product (PC) used in the estimator (Additional file 1).
Prevalence is usually estimated using surveys employing complex sample designs. The prevalence estimates used in this report were made by combining results from several cross-sectional household surveys that used a two-stage cluster sample design representative at the state level following the SMART methodology [10, 20, 21].
Coverage of CMAM programs is often estimated using spatially stratified samples [12, 22–24]. Semi-Quantitative Evaluation of Access and Coverage (SQUEAC) coverage assessments use a Bayesian beta-binomial conjugate analysis in which the conjugate prior contains information that contributes “pseudo-observations” to the analysis [12, 19].
The effective sample size associated with the estimate of a proportion can be calculated from the reported point estimate (p) and its associated upper and lower 95% confidence limits (UCL and LCL).
This calculation is performed to find both n _{ P } and n _{ C } before calculating \( SE\left(\log \widehat{\theta}\right) \).
We used the approach outlined above to find a suitable value for K for the Nigerian CMAM program. Data relating to program admissions (i.e. caseload) in 2014 and 2015 were taken from routine program monitoring reports. Population estimates were made using data from the 2006 Nigerian Census corrected for population growth and migration [25]. Prevalence estimates for SAM were available for 2014 and 2015 [20, 21]. An estimate of program coverage was available from a wide-area Simplified Lot-quality-assurance Evaluation of Access and Coverage (SLEAC) survey completed in early 2014 [12, 26, 27]. The reported coverage from this survey was used for both 2014 and 2015 since it was the only coverage data available. Data were entered and analyzed using Microsoft Excel. This software was used because it is likely to be available and familiar to CMAM program managers. A Microsoft Excel spreadsheet that performs the required calculations is provided as online supporting material. All calculations were checked using the R Language and Environment for Statistical Computing version 3.3.3 [28].
The method used to calculate the 95% confidence limits for the product of prevalence and coverage (PC) is approximate. A less approximate 95% confidence interval (i.e. an interval that contains the true value close to 95% of the time) may be calculated using a bootstrap estimator [29, 30]. Estimates of the incidence correction factor (K) were made using a bootstrap estimator for the product of prevalence and coverage (PC). A percentile bootstrap estimator with one million replicates for prevalence and coverage drawn from appropriate binomial distributions was used [29]. Data were analyzed using the R Language and Environment for Statistical Computing version 3.3.3 [28].
Results
Incidence correction factors for northern Nigeria CMAM program 2014 and 2015 and the data used to calculate them
Year | ||||
---|---|---|---|---|
2014 | 2015 | Data sources | ||
Population | N | 3,550,827 | 4,281,700 | Nigeria census 2006 corrected for population growth and migration. Population is for children aged between 6 and 59 months in districts in which CMAM services were delivered. |
Prevalence^{a} | P | 1.60% (0.50%; 2.71%) | 2.01% (0.82%; 3.19%) | Pooled prevalence from state level SMART surveys |
Program Coverage^{b} | C | 36.6% (32.3%; 40.9%) | 36.6% (32.3%; 40.9%) | Wide-area SLEAC survey |
Observed caseload | L | 320,047 | 398,676 | Routine program monitoring data |
Expected caseload (using K = 1.6) | E _{ K = 1.6} = CNP(1 + K) | 54,063 | 81,897 | C, N, and P as above (C and P expressed as proportions). Calculations are based on K = 1.6 |
Difference (observed − expected) | L − E _{ K = 1.6} | 265,984 | 316,779 | Calculated as the difference between observed caseload (L) and expected caseload (E). |
Prevalence × Coverage | PC | 0.59% (0.29%; 1.18%) | 0.74% (0.40%; 1.34%) | Calculated (see text) |
Incidence correction factor^{c} | \( K=\frac{L}{PCN}-1 \) | 14.39 (6.64; 30.02) | 11.66 (5.94; 22.10) | Calculated (see text) |
Expected caseload (using pooled adjusted incidence correction factor) | E _{ K = 13.02} = CNP(1 + K) | 291,527 | 441,612 | C, N, and P as above (C and P expressed as proportions). Calculations are based on K = 13.02 (see text). |
Difference (observed − expected) | L − E _{ K = 13.02} | 28,520 | −42,936 | Calculated as the difference between observed caseload (L) and expected caseload (E _{ K = 13.02 }). |
Estimates of the incidence correction factor made using two methods to calculate the product of prevalence and coverage
Year | K (approximate) | K (bootstrap) |
---|---|---|
2014 | 14.39 (6.64; 30.02) | 14.72 (7.73; 40.44) |
2105 | 11.66 (5.94; 22.10) | 11.91 (6.17; 27.08) |
Pooled | 13.02 (6.80; 19.25) | 13.32 (6.10; 20.53) |
Discussion
The approach outlined in this document can provide useful estimates of locally appropriate incidence correction factors. Applying the value of K estimated for 2014 to the population, prevalence, and coverage data for 2015 yields a predicted caseload of 484,766 cases. This is a 21.6% overestimate of the observed caseload for 2015. Some of this error may have been due to lower than specified coverage during the implementation phase of additional CMAM programming initiated in early 2015 as part of the ongoing emergency response in Northern Nigeria. This degree of error in caseload prediction is a considerable improvement upon the 79.5% underestimate experienced when using K = 1.6. Applying the pooled estimate of K (i.e. K = 13.02) to the population, prevalence, and coverage data yields predicted caseloads of 291,257 cases and 441,612 cases for 2014 and 2015 respectively. These are a 9.0% underestimate and 10.8% overestimate of the true cases for 2014 and 2015. Error are likely to decrease over time as more annual estimates of K become available. Not all errors have the same consequences. For example, overestimation may have positive consequences if program coverage is limited by stop-start funding and supply breaks caused by underestimation of burden and / or predicted caseload. Underestimation may lead to an under-resourced program in which program activities essential to achieving and maintaining coverage (e.g. community mobilization, community sensitization, and community-based case-finding activities) are neglected in order to maintain core clinical activities. Underestimation, in some cases, may lead to supply breaks necessitating the temporary closure of programs.
Confidence intervals for the bootstrap estimates of the incidence correction factors are wider than when the approximate method is used. Estimates made using the approximate method are likely to be spuriously precise. The use of approximate methods to calculate confidence intervals is, however, a widely accepted practice for many public health applications. The approximate method has the advantage of being easy to implement using software, such as Microsoft Excel, that is available and familiar to CMAM program managers.
Estimates of the incidence correction factor (K) lack precision even when the approximate method is used. For example, the 95% confidence interval for the 2015 estimate of the incidence correction factor (K) using the approximate method ranges between K = 5.94 and K = 22.10. This translates into a 95% confidence interval for the caseload prediction of between about 218 thousand and 728 thousand. This degree of imprecision may limit the utility of the method as a planning tool.
The principal sources of imprecision are in estimates of prevalence and coverage. Improving the precision of estimates of prevalence and / or coverage will improve the precision with which the incidence correction factor (K) is estimated.
The precision of the coverage estimate was not an issue in the work reported here because a large stratified sample was used to estimate coverage with good relative precision (i.e. 23.5%). Precision of coverage estimates may, however, be a problem for smaller programs. We investigated this issue using data from 227 SQUEAC coverage assessments of district-level NGO-delivered CMAM programs performed between January 2010 and July 2015 and provided to us by the Coverage Monitoring Network. The median relative precision for coverage estimates between 40% and 60% was 42.6% (IQR = 38.4%; 48.5%). This is an expected result as SQUEAC coverage assessments are usually designed to estimate coverage with this level of precision [12].
Effect of improved precision of SAM prevalence estimates and coverage estimates of the precision of the estimate of the incidence correction factor (K) using 2015 data from the Nigerian CMAM program
Incidence correction factor (K) | ||||
---|---|---|---|---|
Scenario | Point estimate | 95% LCL | 95% UCL | Relative precision^{a} |
No change | 11.66 | 5.94 | 22.10 | 139.59% |
Reduce half-width of 95% CI for prevalence by 60%^{b} | 11.66 | 7.72 | 17.37 | 82.76% |
Reduce half-width of 95% CI for coverage by 60%^{c} | 11.66 | 6.00 | 21.88 | 136.19% |
Limitations
A key limitation of the work reported here is that coverage data was not current, particularly for 2015.
A limitation of the method described here is that burden and caseload may be influenced by migration into and out of the program area. Rapid and substantial changes in the population of the program area are likely to affect population size (N), prevalence (P), and program coverage (C). Migration may, therefore, result in grossly inaccurate predictions of burden (B) and caseload (L) that are based on estimates of population size (N), prevalence (P), and program coverage (C). Monitoring population movements and adjusting burden and caseload predictions may help to address this problem. Adjustment may also require that additional prevalence and coverage surveys be undertaken. In the case of the Nigerian CMAM program there have been reports of SAM cases entering Nigeria from Niger and being admitted to CMAM sites in districts that border Niger. The effect of this on the work reported here is likely to be small since data for the whole country were used. It is important to note that this may have larger effects on burden (B) and caseload (L) predictions for (e.g.) small NGO-delivered programs operating in border districts.
The assumption that caseload (L) is measured with little or no error may also be a limitation. In the case of the Nigerian CMAM program there have been reports from 3 of the 114 districts in which the program is operating of beneficiaries being registered at more than one CMAM site with the assumed intention of receiving additional food and drugs. New CMAM sites were opened in these districts and some of the double registration may have been due to informal transfers between sites. An informal transfer would have been reported as a new admission at the destination site and, some weeks later, as a defaulting patient at the originating site. The effect of this would have been to increase reported caseload (L). It seems likely that double registration will have had only a small effect on caseload (L) used in the work reported here. This would have caused only a small increase in the estimates for K reported here. The covert nature of some double registrations does mean that the magnitude of any increase will always be difficult to quantify.
Conclusion
The work reported here shows that it is possible to considerably improve predictions of CMAM caseload by applying a simple mathematical model to data that are readily available to program managers. This implies that more accurate predictions of burden may also be made using the same methods and data. The precision of estimates of caseload and burden may be improved by using PROBIT or Bayesian-PROBIT estimators of SAM prevalence.
The implication of this study, and of similar reports based on a variety of approaches (see Table 1), is that the current estimates of SAM burden are likely to be gross underestimates. Applying the pooled incidence correction factor found in this study to the 16 million estimate made using prevalence data yields an estimated global SAM burden of 208 (95% CI = 109; 308) million cases annually. It seems unlikely, however, that the incidence correction factor estimated for the Nigerian CMAM program will be globally applicable. Local estimates of K will be needed to make local prediction of burden and caseload. These local estimates of K could be applied to local estimates of prevalence and population with the results summed in order to estimate global SAM burden.
Given the public health importance of having reliable estimates of burden and caseload and the uncertainties of this approach based on program data, a confirmation of estimates of K using direct estimates of incidence from continuous monitoring of open cohorts and surveillance systems in similar settings may be warranted. Comparison with other indirect methods may also prove useful.
Declarations
Acknowledgements
The authors wish to thank Federal Ministry of Health of Nigeria for providing program data for the analysis.
Funding
No funding was obtained for this study.
Availability of data and materials
Data will be available upon request from the corresponding authors.
Authors’ contributions
AB, ABR, MM conceived the study and drafted the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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