Data analysis
REGRESSION
To estimate the impact of Yathu Yathu on use of an appropriate menstrual product and the four material needs questions, we first described these outcomes. For the four needs questions, we described the responses to the original four categories. Next, we created a binary variable for each question, coding Always as 1 and all other responses as 0. We then compared the five outcomes across the two trial arms. With 10 zones per arm, we used a “two-stage” cluster-level analysis appropriate for CRT with < 15 clusters/arm [First, for each zone, we estimated the proportion of AGYW using an appropriate menstrual product and the proportion responding always to each of the four material needs questions. We then calculated the average value of the cluster-specific proportions. Next, we calculated the prevalence ratio (PR) comparing the Yathu Yathu arm with the control arm. To formally compare the trial arms in an “unadjusted” analysis, we fitted a linear regression model of log(cluster-level proportion) on community and trial arm to obtain a log(PR) comparing the Yathu Yathu with the control arm and estimated the corresponding 95% confidence interval (95%CI). To obtain an estimate adjusted for age and education, we followed the 2-stage procedure [To explore factors associated with ever accessing menstrual products through Yathu Yathu, we restricted analyses to AGYW resident in intervention zones. We coded AGYW as 1 if they ever collected any menstrual products through Yathu Yathu, according to the PPC data, including menstrual products available for “purchase” using points (i.e. reward menstrual products), and 0 otherwise. We used logistic regression, with a fixed effect to account for differences between enumeration zones and adjusted for age at enumeration, to assess the association between the explanatory variables and ever accessing menstrual products. Variables associated with accessing products at the p < 0.10-level in the age-adjusted analysis were included in adjusted analyses.