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

TY - JOUR

T1 - Using cross-sectional surveys to estimate the number of severely malnourished children needing to be enrolled in specific treatment programmes

AU - Dale, Nancy M

AU - Myatt, Mark

AU - Prudhon, Claudine

AU - Briend, André

N1 - CURIS 2017 NEXS 335

PY - 2017/6/1

Y1 - 2017/6/1

N2 - 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.

AB - 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.

KW - Prevalence

KW - Surveys

KW - Nutrition

KW - Severe acute malnutrition

KW - Treatment services

KW - Burden

U2 - 10.1017/s1368980016003578

DO - 10.1017/s1368980016003578

M3 - Journal article

C2 - 28115034

SN - 1368-9800

VL - 20

SP - 1362

EP - 1366

JO - Public Health Nutrition

JF - Public Health Nutrition

IS - 8

ER -