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Background In 2014, the government of Togo implemented a pilot unconditional cash transfer (UCT) program in rural villages that aimed at improving children’s nutrition, health, and protection. It combined monthly UCTs (approximately US$8.40 /month) with a package of community activities (including behavior change communication [BCC] sessions, home visits, and integrated community case management of childhood illnesses and acute malnutrition [ICCM-Nut]) delivered to mother–child pairs during the first “1,000 days” of life. We primarily investigated program impact at population level on children’s height-for-age z-scores (HAZs) and secondarily on stunting (HAZ < −2) and intermediary outcomes including household’s food insecurity, mother–child pairs’ diet and health, delivery in a health facility and low birth weight (LBW), women’s knowledge, and physical intimate partner violence (IPV). Methods and findings We implemented a parallel-cluster–randomized controlled trial, in which 162 villages were randomized into either an intervention arm (UCTs + package of community activities, n = 82) or a control arm (package of community activities only, n = 80). Two different representative samples of children aged 6–29 months and their mothers were surveyed in each arm, one before the intervention in 2014 (control: n = 1,301, intervention: n = 1,357), the other 2 years afterwards in 2016 (control: n = 996, intervention: n = 1,035). Difference-in-differences (DD) estimates of impact were calculated, adjusting for clustering. Children’s average age was 17.4 (± 0.24 SE) months in the control arm and 17.6 (± 0.19 SE) months in the intervention arm at baseline. UCTs had a protective effect on HAZ (DD = +0.25 z-scores, 95% confidence interval [CI]: 0.01–0.50, p = 0.039), which deteriorated in the control arm while remaining stable in the intervention arm, but had no impact on stunting (DD = −6.2 percentage points [pp], relative odds ratio [ROR]: 0.74, 95% CI: 0.51–1.06, p = 0.097). UCTs positively impacted both mothers’ and children’s (18–23 months) consumption of animal source foods (ASFs) (respectively, DD = +4.5 pp, ROR: 2.24, 95% CI: 1.09–4.61, p = 0.029 and DD = +9.1 pp, ROR: 2.65, 95% CI: 1.01–6.98, p = 0.048) and household food insecurity (DD = −10.7 pp, ROR: 0.63, 95% CI: 0.43–0.91, p = 0.016). UCTs did not impact on reported child morbidity 2 week’s prior to report (DD = −3.5 pp, ROR: 0.80, 95% CI: 0.56–1.14, p = 0.214) but reduced the financial barrier to seeking healthcare for sick children (DD = −26.4 pp, ROR: 0.23, 95% CI: 0.08–0.66, p = 0.006). Women who received cash had higher odds of delivering in a health facility (DD = +10.6 pp, ROR: 1.53, 95% CI: 1.10–2.13, p = 0.012) and lower odds of giving birth to babies with birth weights (BWs) <2,500 g (DD = −11.8, ROR: 0.29, 95% CI: 0.10–0.82, p = 0.020). Positive effects were also found on women’s knowledge (DD = +14.8, ROR: 1.86, 95% CI: 1.32–2.62, p < 0.001) and physical IPV (DD = −7.9 pp, ROR: 0.60, 95% CI: 0.36–0.99, p = 0.048). Study limitations included the short evaluation period (24 months) and the low coverage of UCTs, which might have reduced the program’s impact. Conclusions UCTs targeting the first “1,000 days” had a protective effect on child’s linear growth in rural areas of Togo. Their simultaneous positive effects on various immediate, underlying, and basic causes of malnutrition certainly contributed to this ultimate impact. The positive impacts observed on pregnancy- and birth-related outcomes call for further attention to the conception period in nutrition-sensitive programs.
The pilot CT program was administered for 30 months in 2014 by the government of Togo, with financial and technical support from the World Bank and UNICEF. It consisted of monthly cash distributions to women during their children’s first 1,000 days of life (5,000 XOF, i.e., US$8.30/month) combined with behavior change communication (BCC) activities (including home visits and community sensitization meetings) and with the integrated community case management of childhood illnesses and acute malnutrition (ICCM-Nut). The program was implemented in the 5 districts (Dankpen, Doufelgou, Keran, Oti, and Kpendjal) of the Kara and Savanes regions, where the highest rates of acute and chronic malnutrition in children under 5 exist. In 2014, 33.7% and 32.1% of children under 5 years of age were stunted in the Kara and Savanes regions, respectively, versus 27.5% at the national level. The same regions also saw rates of wasting in children of 7.2% and 11.2% versus 6% at the national level. Within the 5 aforementioned districts, the program was implemented in the villages where the “ICCM-Nut program” was running. Carried out since 2011 by the Ministry of Health and UNICEF, this program targeted rural landlocked villages that had poor access to health facilities. In those villages, community health workers (CHWs) were trained and equipped to screen and treat childhood malaria, diarrhea, pneumonia, and acute malnutrition and to lead sensitization meetings on essential family practices (e.g., exclusive breastfeeding, hand washing with soap, and health-seeking behavior). On top of those preexisting activities, BCC sessions focusing on child protection issues (e.g., birth registration, schooling, or fostering) were organized by specifically trained community child protection workers (CCPWs) together with the CTs (S1 Table). CHWs and CCPWs were chosen by the community on the basis of 4 prerequisites: (i) live in the village, (ii) be proficient in French (language used for the trainings), (iii) have the highest possible level of education (have attended at least primary school, know how to read and write), and (iv) be committed to children. Given the minimum educational requirements, most of them were men. The combination of CT and BCC activities (CHWs’ and CCPWs’ sensitization meetings and home visits) was expected to provide women with the essential knowledge and financial resources for the adoption of good childcare practices. CTs were unconditional, but recipient women were strongly encouraged to adopt specific behaviors conducive to their children’s protection and well-being, namely to fulfill at least 4 antenatal visits, register children’s births, enroll children in primary school, keep children younger than 15 at home (no fostering), and attend CCPW and CHW sensitization meetings. Women attending sensitization meetings with assiduity received a bonus of 20,000 XOF (approximately US$33) when exiting the program. To qualify for this reward, women were required to attend at least m + 1 sensitization meetings, where m is the mean number of times women of a given village took part in sensitization meetings. Women who were at least 3 months pregnant and mothers of children aged 0–23 months were eligible to receive the CT. No minimum age limit was set up. If eligible, adolescent women could also benefit from the CT. Women with several eligible children received the intervention for the youngest one only. However, mothers of twins and mothers with both an eligible biological child and an eligible fostered child received the CTs for both children. Mothers with a child aged 24–59 months suffering from severe acute malnutrition were also eligible to receive the CTs. Beneficiary women could enter the program anytime between their second trimester of pregnancy and their child’s second birthday throughout the program duration (knowing that the beneficiaries’ list would be updated every 2 months). Regardless of when they entered, they received the CTs for a minimum of 12 months. Those who entered early in pregnancy took full advantage of the program, benefiting from the CTs for the maximum duration of 30 months. A nonblinded parallel-cluster–randomized controlled trial was carried out to assess the impact of the cash component of the program. A total of 162 villages were randomized into either a control arm benefiting from the ICCM-Nut program (ICCM-Nut and CHWs’ sensitization meetings around essential family practices) and from CCPWs’ package of activities (sensitization meetings and home visits around child protection issues) (n = 80 villages) or into an intervention arm benefiting from the ICCM-Nut program, CCPWs’ package of activities, and CTs (n = 82 villages). Initially, 81 villages were allocated to each arm, but 1 control village was accidentally allocated to the intervention arm while disseminating the randomization results to the communities; it was decided to maintain this village in the intervention arm for ethical reasons. The randomization was stratified by district and carried out by the government of Togo and the World Bank using a random real number, generated with the RAND function in Excel. Two repeated cross-sectional surveys were conducted, one before the first distribution of cash (baseline survey, May 16 to July 4, 2014) and the other after 24 months of program operation (endline survey, May 26 to June 16, 2016) (Fig 1). Representative samples of mothers and their 6- to 29-month–old children in both arms of the study were surveyed in each village. This age range was the best compromise for reasonable theoretical exposure of children to the program (at least 12 months) and stage of exposure (during pregnancy or not later than 6 months of age during infancy) after 2 years of program operation (S1 Fig). In each village, households were randomly selected from the program census database. The latter was compiled prior to the beginning of the study through an exhaustive household census whose aim was to list the households eligible for CTs in each ICCM-Nut village, i.e., those with at least one pregnant woman or a child under 2 years of age. To avoid the overrepresentation of children aged 6–23 months in our sample, we used these households as “starting points” from which we applied a random-route sampling method. At each “starting point,” a household member rolled a die. When the die indicated 5 or 6, we included the household when eligible for the evaluation study (i.e., when at least one child aged 6–29 months was living in the household) or we included the nearest eligible household otherwise. If the die indicated 1, 2, 3, or 4, we systematically selected the eligible household living next to the “starting point” household. If several households were living next to the “starting point” household (at equal distances, in a same compound), one was randomly selected directly in the field. Within selected households, we surveyed all eligible mother–child pairs. If a mother had more than 1 eligible child, the enumerator randomly selected one. This sampling procedure was repeated identically between baseline and endline surveys. CT cluster-randomized controlled trial, Northern Togo, 2014–2016. CT, cash transfer; ICCM-Nut, integrated community case management of childhood illnesses and acute malnutrition. The sample size was calculated to detect a change of 0.20 z-score in height-for-age (HAZ) among 6- to 29-month–old children while considering the following parameters: 5% significance level, 90% of power, 10% of missing/invalid data, 162 villages to be randomized in 2 arms, and an intraclass correlation coefficient of 0.02. To detect a 0.20 HAZ difference between the intervention and control arms at endline (assuming a variance of 1.3 determined from previous studies), a sample size of 1,020 children per arm and per survey was required. This sample size also allowed detecting a difference in the prevalence of stunting of 9% between the 2 arms. This calculation was conservative because it did not account for the district stratification used for randomization. An average of 13 mother–child pairs were surveyed in each village (n = 128), ranging from 5 pairs in small villages (n = 16) to 17 pairs in large villages (n = 18). Data were collected using the same standardized questionnaire at baseline and endline. The questionnaire was administered face-to-face to women and heads of households during home visits by experienced enumerators speaking the local languages of the study area. These enumerators were identified through a highly selective process and extensively trained over 2 weeks. Their training mainly consisted of the exhaustive review, translation, and pretest of the questionnaire; each question was standardized across the different vernacular languages spoken in the study area. Enumerators also learned how to perform anthropometric measurements and how to collect data through tablets. Built upon the program’s theory of change, the questionnaire covered a wide range of intermediary outcomes meant to document the program’s theoretical impact pathways, which are presented in their simplified version in Fig 2 (for a more comprehensive version, please see [27]). At endline, a complementary module was administered to women in order to document how the program rolled out and how it was used by beneficiaries and to collect information on potential unexpected effects. CT cluster-randomized controlled trial, Northern Togo, 2014–2016. BCC, behavior change communication; CT, cash transfer; ICCM-Nut, integrated community case management of childhood illnesses and acute malnutrition. The impact was primarily evaluated on HAZ and secondarily on stunting (HAZ < −2 SD) among 6- to 29-month–old children. Children’s height measurement was standardized according to WHO recommendations [28] and carried out by specifically trained enumerators and assistants. The recumbent length of under-2 children and the standing height of older children were measured to the nearest millimeter using portable devices equipped with height gauges. Children’s ages were reported from official documents when available or from the mother’s memory, using a calendar of local events if necessary. We identified 2 major pathways whereby the program may impact children’s growth and several enabling factors that may facilitate the impact achievement (Fig 2). Indicators along this pathway included the Infant and Young Child Feeding (IYCF) practices indicators, namely the Minimum Dietary Diversity (MDD), the Minimum Meal Frequency (MMF), and the Minimum Acceptable Diet (MAD), which were computed among 6- to 23-month–old children following WHO guidance [29]. We also looked at dietary diversity scores (DDSs) among the whole sample of children, derived from a qualitative multiple-pass 24-h recall and using a 7-food–group classification (grains, roots, and tubers; legumes and nuts; dairy products; flesh foods; eggs; vitamin-A–rich fruits and vegetables; other fruits and vegetables). Likewise, a qualitative multiple-pass 24-h recall performed with mothers was used to compute a DDS for women (WDDS10) using a 10-food–group classification (grains, roots, and tubers; pulses; nuts and seeds; flesh foods; dairy; eggs; dark green leafy vegetables; other vitamin-A–rich fruits and vegetables; other vegetables; other fruits), as recommended by the FAO [30]. The WDDS10 of women of reproductive age (aged 15 to 49) was further dichotomized to compute the Minimum Dietary Diversity for Women (MDD-W) [30,31]. Optimal breastfeeding initiation was computed for children <24 months old (i.e., children who entered the program at birth or before birth). Breastfeeding initiation was considered optimal when 3 conditions were met: the child was given breastmilk within the hour after birth, received colostrum, and was not given any other liquids before initiating breastfeeding. We estimated food insecurity experienced by households over the previous 30 days using the standard Household Food Insecurity Access Scale (HFIAS) [32]. At the child level, we collected data on overall health since birth (as perceived by the mother), morbid episodes over the previous 15 days, and medical monitoring since birth. At the mother level, we collected information on antenatal follow-up, delivery, and postnatal care. In children aged less than 20 months, who had a chance to be exposed to the program in utero, we collected birth weights (BWs) from health documents when available and calculated the proportion of children with low birth weight (LBW) (BW < 2,500 g). The hygiene of individuals and their houses was assessed using spot-check observations [33,34]. In this approach, a list of predetermined conditions (e.g., cleanliness of hands) is observed during a home visit. We considered good hygiene in children to mean their hands, face, and hair were clean (clothes were not taken into account because many children were naked). We deemed mothers’ hygiene good if their hands, face, and clothes were clean (hair was not observed because many mothers were not willing to remove their headscarves). We considered the hygiene of the house to be good if no garbage and no animal feces were observed in the yard. Overall hygiene was scored “good” if the hygiene of the children, their mother, and the yard were all individually rated “good.” See S2 Table. Mothers’ knowledge on breastfeeding, nutrition, child’s health, pregnancy, delivery, hygiene, and birth registration was assessed. Women were attributed points depending on the number of correct answers they provided in each domain; points were summed to calculate a global knowledge score, which was further categorized into terciles to identify women with poor, medium, or good global knowledge. Two dimensions of women’s empowerment were assessed: the decision-making power of women within their households and intimate partner violence (IPV). Decision-making is modeled after questions from the demographic and health surveys (DHSs) [35] and IPV after WHO’s Violence Against Women instrument (VAWI) [36]. One point was given when the woman made decisions on her own for each of the 12 different topics relating to her autonomy, social life, and care for her children. Points were summed to calculate a continuous decision-making score, which was further divided into terciles to identify women with low, moderate, or high decision-making power. Regarding IPV, we estimated the proportion of women who endured controlling behavior, emotional violence, or physical violence from their partners over the past 12 months. Household expenditure was captured through standardized questions similar to that of Household Budget Survey (HBS). Nonfood expenditure was estimated over the last month. Exceptional expenditures (funerals, weddings, religious feast) were removed from the calculation. Food expenditure, including self-consumption, was assessed on the previous day, month, or year according to the type of food considered and then reduced to the last month. Household self-consumption was measured through household utensils and turned into monetary value on the basis of domestic food prices, collected on the markets of the studied area. The average price of each food item was calculated from the prices charged by 3 different vendors. Then, both food and nonfood expenditures were divided by the number of individuals in the household to obtain monthly per capita expenditure. We collected information on household composition and size, on the quality of the house (main source of drinking water, sanitation, main source of energy for cooking, number of rooms), and on the head of household’s sex, level of education, religion, and primary occupation. A youth ratio, defined as the number of household members <15 years of age over the number of persons ≥15 years, and a dependency ratio, defined as the number of people not contributing to household income over the number of people contributing to household income, were computed to account for household structure. Mothers’ ages, pregnancy statuses, education, and marital statuses were collected, as well as the child’s sex and age. Data were collected using Android tablets (through ODK Collect at baseline and Survey CTO at endline), ensuring quality controls at data entry. Collected data were regularly sent to an online server, allowing additional quality checks and feedback for fieldwork improvement. Data quality was also guaranteed by the close field supervision of enumerators throughout the study. Data management was performed with R 3.3.2., and data analysis was performed using Stata 14.2. HAZs and stunting were computed using WHO’s multicenter growth reference standards macro for R [37]. All analyses were conducted using Stata’s svy commands to account for the sampling design (clusters, strata, and sampling weights). Comparability of the trial arms at baseline was tested on sociodemographic characteristics and anthropometric outcomes using linear regression models for continuous variables and logistic regression models for categorical variables. Using the same method, we also compared at endline the coverage of the BCC component between arms and provided a few descriptive statistics on the coverage of the CT component in the intervention arm. The program’s impact on primary and secondary outcomes (HAZ, stunting) was estimated using a difference-in-differences (DD) analysis. DD estimates were computed using linear regressions for HAZ and logistic regressions for stunting. The DD model can be specified in regression form as where Yi is the outcome of interest, phase indicates the time of the survey (baseline/endline), arm indicates the intervention (CT + package of community activities/package of community activities only), phase × arm is the interaction term between the phase and the trial arm, Xi is a matrix containing a set of control variables, μi is the random unobserved error term, β1 represents the time trend common to intervention and control arms β2 accounts for the time-invariant differences between the intervention and control arms, β4 is the vector of coefficients corresponding to the matrix of control variables, and β3 is the DD estimator, which estimates the average program’s effect. For HAZ, the DD estimate is equal to the regression coefficient (β3); for stunting, the DD estimate is based on predicted adjusted values obtained from the regression model by arm and survey. It is reported in percentage points (pp) along with the relative odds ratio (ROR) arising from the logistic regression. Beta and ROR are presented along with their 95% confidence interval (95% CI) and p-value. The program’s impacts on intermediary outcomes within the “food and nutrient” and “health and hygiene” pathways and on enabling environment variables were estimated via the exact same procedures used for HAZ for continuous outcomes and stunting for categorical outcomes. For several outcomes, we restricted the analyses to a subsample according to the child’s age at endline and his/her theoretical exposure to the program (S1 Fig); e.g., the impact on pregnancy-related outcomes was restricted to children aged 6–19 months at endline because children outside this age range had not—or had not sufficiently—been exposed to the program in utero (S1 Fig). To increase the precision of our estimates, we systematically included in our analyses the following covariates: the district in all impact analyses, the child’s age and sex when examining the impact of the program on HAZ and stunting, the child’s age when examining the impact of the program on health outcomes, the child’s sex and maternal height when examining the impact of the program on BW and LBW, and verification (yes/no) of the interviewee responses in a health document when examining the impact of the program on pregnancy-related outcomes. Finally, as recommended when analyzing results of superiority trials, we primarily conducted impact analyses using an intention-to-treat (ITT) approach [38]. Because a high proportion of eligible women did not receive the cash due to implementation issues, we also ran per protocol (PP) analysis. However, we did not base any of our conclusions on PP results; we used such data only to underpin the discussion when relevant [39]. It should be noted that our PP analysis was only based on the CT component of the program. Consequently, we did not abandon any observations in the control arm, and in the intervention arm, we retained all women who received cash at least once (n = 400). All these analyses have been prespecified in the study protocol (S1 Protocol). Ethical clearance was provided by the Ministry of Health of Togo and by the consultative ethic committee of the Institut de Recherche pour le Développement in France. Written informed consent was obtained from all mothers who took part in the study. The trial was registered in the ISRCTN registry under the reference 83330970.
