Results of principal components analysis (PCA) for public and private sector models.^*

<p>*The PCA-generated weights are the eigenvectors of the first principal component; eigenvectors were derived from the correlation matrix in Stata 12.0 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130845#pone.0130845.ref012" target="_blank">12</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130845#pone.0130845.ref013" target="_blank">13</a>]. The first principal component explained 32% and 33% of the variance in the data for the public and private sector knowledge indexes, respectively.</p><p>Results of principal components analysis (PCA) for public and private sector models.^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130845#t003fn001" target="_blank">*</a>}</p

Demographic and clinical characteristics of children in the cohort.

<p>^<i>1</i><i>Indian Rupees</i>,</p><p>^<i>2</i><i>One mother is deceased</i>,</p><p>^<i>3</i><i>Childcare center</i></p><p>Demographic and clinical characteristics of children in the cohort.</p

MOESM1 of Cost-effectiveness analysis of the diarrhea alleviation through zinc and oral rehydration therapy (DAZT) program in rural Gujarat India: an application of the net-benefit regression framework

Additional file 1: Table S1. DAZT intervention components according to activity in Gujarat

Factors associated with prescribing among public sector ASHAs and AWWs.

<p>^a Estimates were calculated using logistic regression with the robust cluster estimator of variance in Stata 12.0 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130845#pone.0130845.ref012" target="_blank">12</a>].</p><p>^b Threshold of dichotomous variable for education was set at the mean number of years of schooling among ASHAs and AWWs (i.e. 10 years).</p><p>Factors associated with prescribing among public sector ASHAs and AWWs.</p

Boxplots* showing ORS, zinc and combined ORS and zinc knowledge index scores for public sector ASHAs and AWWs.

<p>*Boxplots are centered on the mean, which is approximately zero, and display a horizontal line at the median. ** Skewness and kurtosis estimates generated in Stata 12.0 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130845#pone.0130845.ref012" target="_blank">12</a>]: Zinc (-0.91; 3.56); ORS (-0.40; 2.81); Combined (-1.08; 4.14).</p

Binary variables of zinc and ORS knowledge.^*

<p>*Contingency tables assessing the correlation between the binary variables used to construct the knowledge indexes are included in supplementary file <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130845#pone.0130845.s001" target="_blank">S1 Appendix</a>.</p><p>**Providers were not penalized for slight variations in the interpretation of age cut-offs. The following responses were also considered correct: (1) 10 mg for infants 2–6 months of age; (2) 20 mg for children 6–59 months/ 7–59 months/ 6–60 months/ 7–60 months of age</p><p>Binary variables of zinc and ORS knowledge.^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130845#t001fn001" target="_blank">*</a>}</p

Associations between log (base2) days of diarrhea and changes in ASQ-scores.

<p>The graphs were constructed using generalized additive models in R, the solid line depicts the association of the total ASQ-score and log (base2) days of diarrhea. The Y-axis is centered on the mean total ASQ-score. The shaded area spans the 95% confidence interval of this association.</p

Factors associated with prescribing among private sector RMPs.

<p>^a Estimates were calculated using logistic regression with the robust cluster estimator of variance in Stata 12.0 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130845#pone.0130845.ref012" target="_blank">12</a>].</p><p>^b Threshold of dichotomous variable for education was set at the mean number of years of schooling among RMPs (i.e. 14 years).</p><p>Factors associated with prescribing among private sector RMPs.</p

Reported and observed characteristics of AWWs, ASHAs and RMPs.

<p>^a AWWs and ASHAs were sampled from Bihar (N = 165 AWWs; N = 165 ASHAs) and Gujarat (N = 165 AWWs; N = 165 ASHAs); RMPs were sampled from UP.</p><p>^b Prescribing was defined as having advised a specified treatment regardless of whether the product was directly provided during the consultation or advised through another channel.</p><p>^c N (%) responding zinc</p><p>^d Providers not penalized for slight variations in the interpretation of age cut-offs. The following responses were also considered correct: (1) 10 mg for infants 2–6 months of age; (2) 20 mg for children 6–59 months/ 7–59 months/ 6–60 months/ 7–60 months of age</p><p>^e N (%) responding ORS</p><p>^f N (%) responding zinc AND ORS</p><p>Reported and observed characteristics of AWWs, ASHAs and RMPs.</p

Demographic and clinical characteristics of children in the cohort.

<p>^<i>1</i><i>Indian Rupees</i>,</p><p>^<i>2</i><i>One mother is deceased</i>,</p><p>^<i>3</i><i>Childcare center</i></p><p>Demographic and clinical characteristics of children in the cohort.</p