Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from Nigeria
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...
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| Format: | Article |
| Language: | English |
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BMC
2017-11-01
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| Series: | Archives of Public Health |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13690-017-0234-4 |
| _version_ | 1818930206789861376 |
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| author | Assaye Bulti André Briend Nancy M. Dale Arjan De Wagt Faraja Chiwile Stanley Chitekwe Chris Isokpunwu Mark Myatt |
| author_facet | Assaye Bulti André Briend Nancy M. Dale Arjan De Wagt Faraja Chiwile Stanley Chitekwe Chris Isokpunwu Mark Myatt |
| author_sort | Assaye Bulti |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T03:57:01Z |
| format | Article |
| id | doaj.art-89f8d6ca11654132a95baf0d962ecb8c |
| institution | Directory Open Access Journal |
| issn | 2049-3258 |
| language | English |
| last_indexed | 2024-12-20T03:57:01Z |
| publishDate | 2017-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Archives of Public Health |
| spelling | doaj.art-89f8d6ca11654132a95baf0d962ecb8c2022-12-21T19:54:16ZengBMCArchives of Public Health2049-32582017-11-017511810.1186/s13690-017-0234-4Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from NigeriaAssaye Bulti0André Briend1Nancy M. Dale2Arjan De Wagt3Faraja Chiwile4Stanley Chitekwe5Chris Isokpunwu6Mark Myatt7United Nations Children’s Fund (UNICEF)University of Tampere School of Medicine and Tampere University Hospital, University of Tampere, Center for Child Health ResearchUniversity of Tampere School of Medicine and Tampere University Hospital, University of Tampere, Center for Child Health ResearchUnited Nations Children’s Fund (UNICEF)United Nations Children’s Fund (UNICEF)United Nations Children’s Fund (UNICEF), Nepal Country OfficeDepartment of Family Health, Head of Nutrition/SUN Focal Point, Federal Ministry of HealthBrixton HealthAbstract 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.http://link.springer.com/article/10.1186/s13690-017-0234-4Severe acute malnutritionBurdenCaseloadPrevalenceIncidenceNigeria |
| spellingShingle | Assaye Bulti André Briend Nancy M. Dale Arjan De Wagt Faraja Chiwile Stanley Chitekwe Chris Isokpunwu Mark Myatt Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from Nigeria Archives of Public Health Severe acute malnutrition Burden Caseload Prevalence Incidence Nigeria |
| title | Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from Nigeria |
| title_full | Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from Nigeria |
| title_fullStr | Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from Nigeria |
| title_full_unstemmed | Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from Nigeria |
| title_short | Improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition: experiences from Nigeria |
| title_sort | improving estimates of the burden of severe acute malnutrition and predictions of caseload for programs treating severe acute malnutrition experiences from nigeria |
| topic | Severe acute malnutrition Burden Caseload Prevalence Incidence Nigeria |
| url | http://link.springer.com/article/10.1186/s13690-017-0234-4 |
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