Maternal Participation Level in a Nutrition-Sensitive Agriculture Intervention Matters for Child Diet and Growth Outcomes in Rural Ghana

Diana Dallmann, Grace S Marquis, Esi K Colecraft, Roland Kanlisi, Bridget A Aidam, Diana Dallmann, Grace S Marquis, Esi K Colecraft, Roland Kanlisi, Bridget A Aidam

Abstract

Background: Little is known about how the level of program participation affects child nutrition in rural interventions.

Objectives: This study examined the association between participation level in a nutrition-sensitive agriculture intervention and children's diet and anthropometric outcomes in rural Ghana.

Methods: Nutrition Links was a cluster randomized controlled trial (clinicaltrials.gov NCT01985243), which enrolled caregivers with children (aged less than 2 mo in 2014-2015 and less than 18 mo in 2016-2017). Of the 287 caregivers in 19 intervention communities who enrolled, 233 adopted the intervention and received layer poultry, garden inputs, and weekly child feeding education. The egg production and repayment of poultry were monitored, and feed was sold at the weekly meetings. After endline, the nutrition educators rated each woman who adopted the intervention on a scale [very poor (1) to excellent (5)] for: 1) meeting attendance, 2) egg productivity, 3) feed and poultry loan payment, 4) contributions during meetings, and 5) attentiveness towards group members. Participation level was classified as high, medium, and low by dividing the sum of these 5 items into tertiles; 54 women who did not adopt the intervention were classified as "no participation." Generalized mixed linear models tested the difference in changes in children's diet and anthropometric indices between the participation levels and the control category - 213 caregiver-child dyads in 20 communities who received standard-of-care health and agricultural services.

Results: Compared with the control category, only high participation was associated with egg consumption [adjusted OR (aOR) = 3.03; 95% CI: 1.15, 7.94]. Both medium and high participation levels were associated with length-for-age z-scores (LAZ)/height-for-age z-scores (HAZ) [adjusted β-coefficients (aβ) = 0.44; 95% CI: 0.16, 0.72 and 0.40; 95% CI: 0.12, 0.67, respectively].

Conclusion: These results highlight the importance of promoting and monitoring the level of beneficiary participation to estimate the full potential of nutrition-sensitive agriculture interventions to improve nutritional outcomes.

Keywords: Ghana; child; diet; eggs; growth; infant; low-income population; nutrition program implementation; nutrition-sensitive agriculture; participation.

© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition.

References

    1. Ghana Statistical Service (GSS) . Multiple Indicator Cluster Survey (MICS 2006), Survey Findings Report. Accra (Ghana): GSS; 2006.
    1. Ghana Statistical Service (GSS) . Multiple Indicator Cluster Survey (MICS 2017/18), Survey Findings Report. Accra (Ghana): GSS; 2018.
    1. Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, Ezzati M, Grantham-McGregor S, Katz J, Martorell Ret al. . Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet. 2013;382(9890):427–51.
    1. Cooke E, Hague S, McKay A. The Ghana Poverty and Inequality Report: Using the 6th Ghana Living Standards Survey. UNICEF, University of Sussex UK, Ashesi University College Ghana; 2016.
    1. Ruel MT, Alderman H. Nutrition-sensitive interventions and programmes: how can they help to accelerate progress in improving maternal and child nutrition?. Lancet. 2013;382(9891):536–51.
    1. Sharma IK, Di Prima S, Essink D, Broerse JEW. Nutrition-sensitive agriculture: a systematic review of impact pathways to nutrition outcomes. Adv Nutr. 2021;12(1):251–75.
    1. Berti PR, Krasevec J, FitzGerald S. A review of the effectiveness of agriculture interventions in improving nutrition outcomes. Public Health Nutr. 2004;7(5):599–609.
    1. Masset E, Haddad L, Cornelius A, Isaza-Castro J. Effectiveness of agricultural interventions that aim to improve nutritional status of children: systematic review. BMJ. 2012;344(jan17 1):d8222.
    1. Webb Girard A, Self JL, McAuliffe C, Olude O. The effects of household food production strategies on the health and nutrition outcomes of women and young children: a systematic review. Paediatr Perinat Epidemiol. 2012;26:(Suppl 1):205–22.
    1. Webb P, Kennedy E. Impacts of agriculture on nutrition: nature of the evidence and research gaps. Food Nutr Bull. 2014;35(1):126–32.
    1. Ruel MT, Quisumbing AR, Balagamwala M. Nutrition-sensitive agriculture: what have we learned so far?. Glob Food Sec. 2018;17:128–53.
    1. Menon P, Covic NM, Harrigan PB, Horton SE, Kazi NM, Lamstein S, Neufeld L, Oakley E, Pelletier D. Strengthening implementation and utilization of nutrition interventions through research: a framework and research agenda. Ann NY Acad Sci. 2014;1332(1):39–59.
    1. Rossi PH, Lipsey MW, Henry GT. Evaluation: a systematic approach. 8th ed.Thousand Oaks (CA): SAGE Publications; 2018.
    1. Bell SH, Peck LR. On the feasibility of extending social experiments to wider applications. JMDE. 2016;12(27):93–111.
    1. Penny ME, Creed-Kanashiro HM, Robert RC, Narro MR, Caulfield LE, Black RE. Effectiveness of an educational intervention delivered through the health services to improve nutrition in young children: a cluster-randomised controlled trial. Lancet. 2005;365(9474):1863–72.
    1. Robert RC, Gittelsohn J, Creed-Kanashiro H, Penny ME, Caulfield LE, Narro MR, Black RE. Process evaluation determines the pathway of success for a health center-delivered, nutrition education intervention for infants in Trujillo. J Nutr. 2006;136(3):634–41.
    1. Bezner Kerr R, Berti PR, Shumba L. Effects of a participatory agriculture and nutrition education project on child growth in northern Malawi. Public Health Nutr. 2011;14(8):1466–72.
    1. de Brauw A, Eozenou P, Moursi M. Programme participation intensity and children's nutritional status: evidence from a randomised control trial in Mozambique. J Dev Stud. 2015;51(8):996–1015.
    1. Century J, Rudnick M, Freeman C. A framework for measuring fidelity of implementation: a foundation for shared language and accumulation of knowledge. Am J Eval. 2010;31(2):199–218.
    1. Borrelli B, Sepinwall D, Ernst D, Bellg AJ, Czajkowski S, Breger R, DeFrancesco C, Levesque C, Sharp DL, Ogedegbe G. A new tool to assess treatment fidelity and evaluation of treatment fidelity across 10 years of health behavior research. J Consult Clin Psychol. 2005;73(5):852.
    1. Resnick B, Inguito P, Orwig D, Yahiro JY, Hawkes W, Werner M, Zimmerman S, Magaziner J. Treatment fidelity in behavior change research: a case example. Nurs Res. 2005;54(2):139–43.
    1. Forman SG, Shapiro ES, Codding RS, Gonzales JE, Reddy LA, Rosenfield SA, Sanetti LMH, Stoiber KC. Implementation science and school psychology. Sch Psychol Q. 2013;28(2):77.
    1. Carroll C, Patterson M, Wood S, Booth A, Rick J, Balain S. A conceptual framework for implementation fidelity. Implement Sci. 2007;2(1):40.
    1. Durlak JA, DuPre EP. Implementation matters: a review of research on the influence of implementation on program outcomes and the factors affecting implementation. Am J Community Psychol. 2008;41(3–4):327–50.
    1. Rowbotham S, Conte K, Hawe P. Variation in the operationalisation of dose in implementation of health promotion interventions: insights and recommendations from a scoping review. Implement Sci. 2019;14(1):56.
    1. Marquis GS, Colecraft EK, Kanlisi R, Aidam BA, Atuobi-Yeboah A, Pinto C, Aryeetey R. An agriculture-nutrition intervention improved children's diet and growth in a randomized trial in Ghana. Matern Child Nutr. 2018;14:e12677.
    1. Ghana Statistical Service (GSS) . 2010 Population and Housing Census. District analytical report. Upper Manya Krobo District. Accra (Ghana): GSS; 2014.
    1. De Vries J. Passing on the gift as an approach to sustainable development programmes. Dev Pract. 2012;22(3):373–84.
    1. Baumgartner TA. Estimating the stability reliability of a score. Meas Phys Educ Exerc Sci. 2000;4(3):175–8.
    1. Neuman WL. Social research methods: qualitative and quantitative approaches. 7th ed.Harlow (ND): Pearson Education, Inc; 2014.
    1. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155–63.
    1. Bland JM, Altman DG. Statistics notes: Cronbach's alpha. BMJ. 1997;314(7080):572.
    1. DeVellis RF. Scale development: theory and applications. 4th ed.Los Angeles (CA): SAGE Publications; 2017.
    1. WHO . Indicators for Assessing Infant and Young Child Feeding Practices: Part 1: definitions: conclusions of a consensus meeting held. 6–8 November 2007inWashington DC, USA: WHO; 2008.
    1. WHO . WHO child growth standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: methods and development. Geneva (Switzerland): WHO; 2006. Report No.: 924154693X.
    1. ELCSA Scientific Committee . Escala Latinoamericana y Caribena de Seguridad Alimentaria (ELCSA): Manual de Uso y Aplicaciones. Rome (Italy): FAO; 2012.
    1. Sharpe D. Chi-square test is statistically significant: now what?. Pract Assess Res Evaluation. 2015;20:Article 8.
    1. SAS Institute Inc. SAS/STAT® 15.1 user's guide. Cary (NC): SAS Institute Inc; 2018.
    1. Lee S, Lee DK. What is the proper way to apply the multiple comparison test?. Korean J Anesthesiol. 2018;71(5):353.
    1. Stroup WW. Mixed model procedures to assess power, precision, and sample size in the design of experiments. Proceedings-Biopharmaceutical Section. Alexandria (VA): American Statistical Association; 1999. p. 15–24.
    1. Kim SS, Nguyen PH, Yohannes Y, Abebe Y, Tharaney M, Drummond E, Frongillo EA, Ruel MT, Menon P. Behavior change interventions delivered through interpersonal communication, agricultural activities, community mobilization, and mass media increase complementary feeding practices and reduce child stunting in Ethiopia. J Nutr. 2019;149(8):1470–81.
    1. Suresh S, Paxton A, Pun BK, Gyawali MR, Kshetri ID, Rana PP, Cunningham K. Degree of exposure to interventions influences maternal and child dietary practices: evidence from a large-scale multisectoral nutrition program. PLoS One. 2019; 14(8):e0221260.
    1. Passarelli S, Ambikapathi R, Gunaratna NS, Madzorera I, Canavan CR, Noor AR, Worku A, Berhane Y, Abdelmenan S, Sibanda S. A chicken production intervention and additional nutrition behavior change component increased child growth in Ethiopia: a cluster-randomized trial. J Nutr. 2020;150(10):2806–17.
    1. Gelli A, Margolies A, Santacroce M, Roschnik N, Twalibu A, Katundu M, Moestue H, Alderman H, Ruel M. Using a community-based early childhood development center as a platform to promote production and consumption diversity increases children's dietary intake and reduces stunting in Malawi: a cluster-randomized trial. J Nutr. 2018;148(10):1587–97.
    1. Kadiyala S, Harris J, Headey D, Yosef S, Gillespie S. Agriculture and nutrition in India: mapping evidence to pathways. Ann NY Acad Sci. 2014;1331(1):43–56.
    1. Fagley NS. Applied statistical power analysis and the interpretation of nonsignificant results by research consumers. J Couns Psychol. 1985;32(3):391.
    1. Sodjinou E, Henningsen A. Community-based management and interrelations between different technology adoption decisions: innovations in village poultry farming in Western Africa. Copenhagen (Denmark): University of Copenhagen, Department of Food and Resource Economics (IFRO; ); 2012.
    1. Rogers EM. Diffusion of preventive innovations. Addict Behav. 2002;27(6):989–93.
    1. Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191.
    1. Damron D, Langenberg P, Anliker J, Ballesteros M, Feldman R, Havas S. Factors associated with attendance in a voluntary nutrition education program. Am J Health Promot. 1999;13(5):268–75.
    1. Kumar N, Nguyen PH, Harris J, Harvey D, Rawat R, Ruel MT. What it takes: evidence from a nutrition- and gender-sensitive agriculture intervention in rural Zambia. J Dev Effect. 2018;10(3):341–72.
    1. Santoso M, Bezner Kerr R, Kassim N, Mtinda E, Martin H, Hoddinott J, Young S. Predictors of program participation in a nutrition-sensitive agroecological intervention in Singida. Curr Dev Nutr. 2020;4(Supplement_2):903.
    1. Tavakol M, Dennick R. Making sense of Cronbach's alpha. Int J Med Educ. 2011;2:53–5.
    1. Nguyen PH, Menon P, Keithly SC, Kim SS, Hajeebhoy N, Tran LM, Ruel MT, Rawat R. Program impact pathway analysis of a social franchise model shows potential to improve infant and young child feeding practices in Vietnam. J Nutr. 2014;144(10):1627–36.

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