Nancy M Dale1, Mark Myatt2, Claudine Prudhon3, André Briend1. 1. 1Tampere Centre for Child Health Research,University of Tampere and Tampere University Hospital,Lääkärinkatu 1,33014 University of Tampere,Finland. 2. 2Brixton Health,Llawryglyn,UK. 3. 3Save the Children,London,UK.
Abstract
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.
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.
Entities:
Keywords:
Burden; Nutrition; Prevalence; Severe acute malnutrition treatment services; Surveys
Authors: Rachel P Chase; Marko Kerac; Angeline Grant; Mark Manary; André Briend; Charles Opondo; Jeanette Bailey Journal: PLoS One Date: 2020-06-03 Impact factor: 3.240
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