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Nutrition-sensitive agriculture programmes have the potential to improve child nutrition outcomes, but livestock intensification may pose risks related to water, sanitation and hygiene (WASH) conditions. We assessed the impact of SELEVER, a nutrition- and gender-sensitive poultry intervention, with and without added WASH focus, on hygiene practices, morbidity and anthropometric indices of nutrition in children aged 2−4 years in Burkina Faso. A 3-year cluster randomised controlled trial was implemented in 120 villages in 60 communes (districts) supported by the SELEVER project. Communes were randomly assigned using restricted randomisation to one of three groups: (1) SELEVER intervention (n = 446 households); (2) SELEVER plus WASH intervention (n = 432 households); and (3) control without intervention (n = 899 households). The study population included women aged 15−49 years with an index child aged 2−4 years. We assessed the effects 1.5-years (WASH substudy) and 3-years (endline) post-intervention on child morbidity and child anthropometry secondary trial outcomes using mixed effects regression models. Participation in intervention activities was low in the SELEVER groups, ranging from 25% at 1.5 years and 10% at endline. At endline, households in the SELEVER groups had higher caregiver knowledge of WASH-livestock risks (∆ = 0.10, 95% confidence interval [CI] [0.04−0.16]) and were more likely to keep children separated from poultry (∆ = 0.09, 95% CI [0.03−0.15]) than in the control group. No differences were found for other hygiene practices, child morbidity symptoms or anthropometry indicators. Integrating livestock WASH interventions alongside poultry and nutrition interventions can increase knowledge of livestock-related risks and improve livestock-hygiene-related practices, yet may not be sufficient to improve the morbidity and nutritional status of young children.

PR, IFPRI3; ISI; CRP4; 2 Promoting Healthy Diets and Nutrition for all; DCA; SELEVER; IFPRIOA, PHND; A4NH, CGIAR Research Program on Agriculture for Nutrition and Health (A4NH), Background: SELEVER is a nutrition- and gender-sensitive poultry value chain project designed and implemented by international NGO Tanager which consists of poultry market facilitation and behavior change activities aiming at increasing poultry production and improving diets without free inputs transfer. Objectives: The study aimed at assessing the impact of SELEVER on diets of women and children during the lean season. Methods: Within a cluster-randomized controlled trial, 45 communes were assigned to one of three arms, including 1) SELEVER interventions; 2) SELEVER with an intensive hygiene and sanitation component (SELEVER + WASH); and 3) a control group without intervention. Two rounds of survey were conducted 2 years apart during the lean season. Primary dietary outcomes were the probability of adequacy (PA) of iron, zinc and vitamin A intakes, mean PA (MPA) of 11 micronutrients and individual dietary diversity score collected through quantitative 24-hour recall in longitudinal samples of women and index children (2–4 years old) in 1,054 households; and minimum acceptable diet in the repeated cross-sectional sample of their younger sibling aged 6–23 months. Impacts were assessed by intention-to-treat analysis of covariance. Results: Relative to control, SELEVER interventions (groups 1 + 2) increased the PA of iron intakes in women by 1.8 pp (P = 0.030). We found no further impact on primary outcomes, although eggs consumption increased in index children (+0.73 pp, P = 0.010; +0.69 kcal/d, P = 0.036). Across the three groups, we observed negative effects of SELEVER on the PA of zinc intakes in women relative to SELEVER + WASH (-4.1 pp, P = 0.038), and on a variety of secondary dietary outcomes relative to both other groups. The study was registered on the ISCRCTN registry (ISRCTN16686478). Conclusions: information-only-based value-chain interventions may not have meaningful positive effects on diets of women and children in the lean season in settings with large

PR, IFPRI3; DCA; CRP4; IFPRIOA; ISI, PHND; A4NH, CGIAR Research Program on Agriculture for Nutrition and Health (A4NH), Data on dietary nutrient intakes of adolescents in low‐ and middle‐income countries (LMIC) is lacking partly due to the absence of validation studies of the 24‐h recall method in adolescents. We conducted a validation study of 24‐h recall (24HR) compared with observed weighed records (OWR) in adolescents (n = 132, 10–11 years; n = 105, 12–14 years). Dietary data were collected for the same day by both methods by conducting the 24HR the day after the OWR. For OWR, all foods consumed by adolescents from the first to last meal of the day were weighed; for 24HR adolescents reported foods consumed using portion aids. Food intakes were converted to nutrients. Nutrient intakes by both methods were tested for equivalence by comparing the ratios (24HR/OWR) with equivalence margins of within ±10%, 15% and 20% of the ratio. Prevalences of inadequacy (POIs) were obtained using the NCI method. Mean ratios for energy were 0.88 and 0.92, for younger and older adolescents, respectively, and other nutrients ranged between 0.84 and 1.02. Energy intakes were equivalent within the 15% bound, and most nutrients fell within the 20% bound. POI was overestimated by 24HR, but differences were less than 25 percentage points for most nutrients. Half of adolescents omitted foods in recalls, mainly sweet or savoury snacks, fruits and beverages. Our study showed that adolescents underestimated intakes by 24HR; however, the degree of underestimation was generally acceptable for 12–14‐year‐olds within a bound of 15%. Errors could possibly be reduced with further training and targeted probing.

Poultry production in low income countries provides households with nutrient-rich meat and egg products, as well as cash income. However, traditional production systems present potential health and nutrition risks because poultry scavenging around household compounds may increase children's exposure to livestock-related pathogens. Data from a cross-sectional survey were analysed to examine associations between poultry, water, sanitation, and hygiene practices, and anthropometric indicators in children (6–59 months; n = 3,230) in Burkina Faso. Multilevel regression was used to account for the hierarchical nature of the data. The prevalence of stunting and wasting in children 6–24 months was 19% and 17%, respectively, compared with a prevalence of 26% and 6%, respectively, in children 25–60 months. Over 90% of households owned poultry, and chicken faeces were visible in 70% of compounds. Caregivers reported that 3% of children consumed eggs during a 24-hr recall. The presence of poultry faeces was associated with poultry flock size, poultry-husbandry and household hygiene practices. Having an improved water source and a child visibly clean was associated with higher height-for-age z scores (HAZ). The presence of chicken faeces was associated with lower weight-for-height z scores, and no associations were found with HAZ. Low levels of poultry flock size and poultry consumption in Burkina Faso suggest there is scope to expand production and improve diets in children, including increasing chicken and egg consumption. However, to minimize potential child health risks associated with expanding informal poultry production, research is required to understand the mechanisms through which cohabitation with poultry adversely affects child health and design interventions to minimize these risks.

Non-PR, IFPRI1; CRP4; 2 Promoting Healthy Diets and Nutrition for all; Capacity Strengthening; DCA; SELEVER, PHND; A4NH, CGIAR Research Program on Agriculture for Nutrition and Health (A4NH), The SELEVER study is a five-year impact evaluation in Burkina Faso designed to address key knowledge gaps on the impact of poultry value chain interventions on the diets, health and nutritional status of women and children.

PR, IFPRI3; ISI; DCA; CRP4; SELEVER, PHND; A4NH, CGIAR Research Program on Agriculture for Nutrition and Health (A4NH), Poultry production in low income countries provides households with nutrient‐rich meat and egg products, as well as cash income. However, traditional production systems present potential health and nutrition risks because poultry scavenging around household compounds may increase children's exposure to livestock‐related pathogens. Data from a cross‐sectional survey were analysed to examine associations between poultry, water, sanitation, and hygiene practices, and anthropometric indicators in children (6–59 months; n = 3,230) in Burkina Faso. Multilevel regression was used to account for the hierarchical nature of the data. The prevalence of stunting and wasting in children 6–24 months was 19% and 17%, respectively, compared with a prevalence of 26% and 6%, respectively, in children 25–60 months. Over 90% of households owned poultry, and chicken faeces were visible in 70% of compounds. Caregivers reported that 3% of children consumed eggs during a 24‐hr recall. The presence of poultry faeces was associated with poultry flock size, poultry‐husbandry and household hygiene practices. Having an improved water source and a child visibly clean was associated with higher height‐for‐age z scores (HAZ). The presence of chicken faeces was associated with lower weight‐for‐height z scores, and no associations were found with HAZ. Low levels of poultry flock size and poultry consumption in Burkina Faso suggest there is scope to expand production and improve diets in children, including increasing chicken and egg consumption. However, to minimize potential child health risks associated with expanding informal poultry production, research is required to understand the mechanisms through which cohabitation with poultry adversely affects child health and design interventions to minimize these risks.

BackgroundThe SELEVER study is designed to evaluate the impact of an integrated agriculture–nutrition package of interventions (including poultry value chain development, women’s empowerment activities, and a behavior change communications strategy to promote improved diets and feeding, care, and hygiene practices) on the diets, health, and nutritional status of women and children in Burkina Faso. This paper presents the rationale and study design.MethodsThe impact evaluation involves a cluster randomized controlled trial design that will be implemented in 120 rural communities/villages within 60 communes supported by SELEVER in the Boucle de Mouhoun, Centre-Ouest, and Haut-Bassins regions of Burkina Faso. Communities will be randomly assigned to one of three treatment arms, including: (1) SELEVER intervention group; (2) SELEVER with an intensive WASH component; and (3) control group without intervention. Primary outcomes include the mean probability of adequacy of diets for women and children (aged 2–4 years at baseline), infant and young child feeding practices of caregivers of children aged 0–2 years, and household poultry production and sales. Intermediate outcomes along the agriculture and nutrition pathways will also be measured, including child nutrition status and development. The evaluation will follow a mixed-methods approach, including a panel of child-, household-, community-, and market-level surveys, and data collection points during post-harvest and lean seasons, as well as one year after implementation completion to examine sustainability.DiscussionTo our knowledge, this study is the first to rigorously examine from a food systems perspective, the simultaneous impact of scaling-up nutrition-specific and nutrition-sensitive interventions through a livestock value-chain and community-intervention platform, across nutrition, health, and agriculture domains. The findings of this evaluation will provide evidence to support the design of market-based nutri

PR, IFPRI3; ISI; CRP4; B.1 Integrated Agriculture, Nutrition, and Health Programs and Policies; B.2 Value Chains for Enhances Nutrition; SELEVER, PHND; A4NH, CGIAR Research Program on Agriculture for Nutrition and Health (A4NH)

BACKGROUND: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India. RESULTS: Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified. CONCLUSION: The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.

BACKGROUND: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India. RESULTS: Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified. CONCLUSION: The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.

BACKGROUND: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India. RESULTS: Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified. CONCLUSION: The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.

BACKGROUND: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India. RESULTS: Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified. CONCLUSION: The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.

BACKGROUND: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India. RESULTS: Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified. CONCLUSION: The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.

BACKGROUND: Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India. RESULTS: Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women's self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified. CONCLUSION: The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women's self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.