Additional file 2: Figure S2. of Anion gap as a prognostic tool for risk stratification in critically ill patients – a systematic review and meta-analysis

Forest plot of odds ratios (ORs) for corrected AG predicting mortality. Forest plot of a fixed effects meta-analysis of ORs derived by univariate logistic regression for the corrected AG predicting mortality; I2 = 5 %. In view of the high heterogeneity in meta-analyses of other effect measures a pooled effect estimate is not shown. (DOCX 605 kb

Additional file 1: Figure S1. of Anion gap as a prognostic tool for risk stratification in critically ill patients – a systematic review and meta-analysis

Forest plot of area under the ROC curves (AUCs) for corrected AG predicting mortality. Forest plot of a random effects meta-analysis of AUCs for the corrected AG predicting mortality; I2 = 67 %. In view of the high heterogeneity a pooled effect estimate is not shown. (PDF 9 kb

Additional file 5: Figure S5. of Anion gap as a prognostic tool for risk stratification in critically ill patients – a systematic review and meta-analysis

Funnel plot of area under the ROC curve (AUC). Funnel plot of the standard error of AUC (SE(AUC)) against the AUC for observed AG. SE = standard error. (DOCX 47 kb

Additional file 4: Figure S4. of Anion gap as a prognostic tool for risk stratification in critically ill patients – a systematic review and meta-analysis

Funnel plot of mean differences. Funnel plot of the standard error of mean difference (SE(MD)) against the mean difference for observed AG. MD = mean difference; SE = standard error. (DOCX 46 kb

Additional file 3: Figure S3. of Anion gap as a prognostic tool for risk stratification in critically ill patients – a systematic review and meta-analysis

Forest plot of mean differences for corrected AG predicting mortality. Forest plot of mean differences in corrected AG between survivors and non-survivors; I2 = 0 %. In view of the high heterogeneity in meta-analyses of other effect measures a pooled effect estimate is not shown. (DOCX 491 kb

Discrete-time logistic regression results for neonatal mortality, rural northern India^‡, 2005–06.

<p>Note:</p>‡<p>includes Rajasthan, Uttaranchal, Uttar Pradesh, Madhya Pradesh, Chhattisgarh, Bihar, and Jharkhand.</p><p>CI – confidence interval.</p><p>Control variables included wealth quintiles, religion, caste, mother's education, and sex of the newborn.</p

Binary logistic regression analysis results for factors affecting utilization of two or more tetanus toxoid vaccinations, rural Northern India^‡, 2005–06.

‡<p>includes Rajasthan, Uttaranchal, Uttar Pradesh, Madhya Pradesh, Chattisgarh, Bihar, and Jharkhand.</p><p>CI – confidence interval.</p

The prevalence of pregnancy health-care services provided to mothers and other birth related characteristics of infants born in five years preceding NFHS 3, rural northern India^‡, 2005–06.

<p>Note:</p>‡<p>includes Rajasthan, Uttaranchal, Uttar Pradesh, Madhya Pradesh, Chattisgarh, Bihar, and Jharkhand.</p><p>The total number varies between categories because some values are missing.</p

Percentage of births in facilities by wealth quintile in urban and rural populations; examples of countries in each typology.

<p>Percentage of births in facilities by wealth quintile in urban and rural populations; examples of countries in each typology.</p