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

Boxplots* showing ORS, zinc and combined ORS and zinc knowledge index scores for private sector RMPs.

<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 (0.72; 2.29); ORS (-0.05; 1.93); Combined (0.92; 2.89).</p

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

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

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

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

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

Estimated number of episodes due to hospitalizations, and deaths due to <i>Shigellae</i> among older children and adults in Africa and South Asia using Method 2.

∧<p>Median incidence data not available; review used SEAR incidence rates applied to AFR population to get total # annual episodes <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002705#pntd.0002705-FischerWalker2" target="_blank">[6]</a>.</p>∧∧<p>2010 population among persons ≥5 yrs of age. Source: UN Population Division <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002705#pntd.0002705-United1" target="_blank">[9]</a>.</p>*<p>Calculated using weighted mean % positive from outpatient studies reporting >4 pathogens (i.e. ETEC: 4.6%; <i>Shigellae</i>: 9.3%) <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002705#pntd.0002705-FischerWalker2" target="_blank">[6]</a>.</p>**<p>Calculated using proportion of annual cases due to <i>Shigellae</i> reported at a treatment facility (i.e. 0.053) <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002705#pntd.0002705-Bardhan1" target="_blank">[11]</a>.</p>***<p>Calculated by applying case fatality rate for hospitalized cases due to <i>Shigellae</i> (i.e. 0.33%) <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002705#pntd.0002705-Bardhan1" target="_blank">[11]</a>.</p

Estimated number of deaths due to <i>Shigellae</i> and ETEC among older children and adults in Africa and South Asia using Method 1.

∧<p>Papers identified by previously published systematic review <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002705#pntd.0002705-FischerWalker1" target="_blank">[2]</a>.</p>∧∧<p>Number of deaths per year among persons ≥5 yrs of age in countries identified by WHO regions AFR and SEAR; calculated by dividing 2005–2010 deaths by 5. Source: UN Population Division <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002705#pntd.0002705-United1" target="_blank">[9]</a>.</p>*<p>Calculated by multiplying the age-specific median % deaths attributable to diarrhea by the age-specific total # deaths per year.</p>**<p>Calculated by multiplying median % of diarrhea hospitalizations due to <i>Shigellae</i> (i.e. 4.3%) by total # of diarrhea deaths in each region <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002705#pntd.0002705-FischerWalker2" target="_blank">[6]</a>.</p>***<p>Calculated by multiplying median % of diarrhea hospitalizations due to ETEC (i.e. 9.5%) by total # of diarrhea deaths in each region <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002705#pntd.0002705-FischerWalker2" target="_blank">[6]</a>.</p

Results of the systematic review.

<p>Footnotes: ¹Main reasons for exclusion: no outcome of interest (n = 23); review paper (n = 5); under-five data only (n = 5); data combined across under-five and older age groups (n = 4); retrospective review of hospital records (n = 3); special population (n = 1). ² Source: Fischer Walker CL, Black RE (2010) Diarrhoea morbidity and mortality in older children, adolescents, and adults. Epidemiol Infect 138: 1215–1226.</p