Descriptive statistics.

<p>Sources: Data from the population Census 1991 & 2000, the Institute of Applied Economic Research (IPEA) and Sousa A, et al. 2010 for neonatal mortality.</p><p>Note: For all variables, differences in the mean values between years are statistically significant except for the density of physicians per 1000 population.</p

Fixed-effect regression models of neonatal mortality rates for the MCA in all the sample and separated by poor and non-poor areas in Brazil, 1991–2000.

<p>Sources: Author’s calculation using data from the population Census 1991 & 2000, the Institute of Applied Economic Research (IPEA) and Sousa A, et al. 2010 for neonatal mortality.</p><p>Note: The models control for state fixed effects not presented in the table. Estimates were produced using robust standard errors to adjust for the presence of heteroscedasticity. We used the log of neonatal mortality as dependant variable. Statistical significance with a *p<0.05; **p<0.01; ***p<0.001. Poor refers to minimum comparable areas (MCA) with more than 50% of population below the poverty line, and non-poor otherwise. In all models, differences in the coefficients between categories of health workers are statically significant except for the densities of physicians and nurse professionals. Differences in the coefficients between poor and non-poor areas are also statistically significant. Other covariates such as the proportion of adult women (over age 15) with less than five years of education (average years) were also explored but not considered for the final analysis because of multicolinearity and for having less explanatory power than the variables finally included in the models.</p

Effect of skilled and unskilled health workers availability on neonatal mortality in poor and rich areas.

<p>Sources: Author’s calculation using the output from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074772#pone-0074772-t002" target="_blank">Table 2</a>.</p><p>Note: Skilled health workers refers to physicians & nurse professional and unskilled health workers to nurse associate & community health workers. Poor refers to minimum comparable areas (MCA) with more than 50% of population below the poverty line, and non-poor otherwise. The explained reduction by skilled health workers for poor and non-poor areas is the sum of the marginal effects estimated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074772#pone-0074772-t003" target="_blank">Table 3</a> for physicians and nurse professionals. Similarly, the explained reduction by unskilled health workers is the sum of the marginal effects for nurse associate and community health worker.</p

Trends of the neonatal mortality rate per 1000–2005.

<p>Sources: Author’s calculation using data from the population Census 1991 & 2000, the Institute of Applied Economic Research (IPEA), DATASUS 2005, Sousa A, et al. 2010 for neonatal mortality 1991 & 2000 and projected estimates of neonatal mortality rate 2005 from output <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074772#pone-0074772-t002" target="_blank">table 2</a>. Note: X axis = year. Y axis left = neonatal mortality rate per 1000 lb. Y axis right = health workers density per 1000 pop. Green square = neonatal mortality rate for poor areas. Blue diamond = neonatal mortality rate for non-poor areas. Pink cross = health workers density for non- poor areas. Orange cross = health workers density for poor areas.</p

Estimating Diarrhea Mortality among Young Children in Low and Middle Income Countries

<div><h3>Background</h3><p>Diarrhea remains one of the leading causes of morbidity and mortality among children under 5 years of age, but in many low and middle-income countries where vital registration data are lacking, updated estimates with regard to the proportion of deaths attributable to diarrhea are needed.</p> <h3>Methods</h3><p>We conducted a systematic literature review to identify studies reporting diarrhea proportionate mortality for children 1–59 mo of age published between 1980 and 2009. Using the published proportionate mortality estimates and country level covariates we constructed a logistic regression model to estimate country and regional level proportionate mortality and estimated uncertainty bounds using Monte-Carlo simulations.</p> <h3>Findings</h3><p>We identified more than 90 verbal autopsy studies from around the world to contribute data to a single-cause model. We estimated diarrhea proportionate mortality for 84 countries in 6 regions and found diarrhea to account for between 10.0% of deaths in the Americas to 31.3% of deaths in the South-east Asian region.</p> <h3>Discussion</h3><p>Diarrhea remains a leading cause of death for children 1–59 mo of age. Published literature can be used to create a single-cause mortality disease model to estimate mortality for countries lacking vital registration data.</p> </div

Comparison of country specific diarrhea-proportionate mortality estimates by country for the single cause-model vs. Lancet estimates [<b>5</b>].

<p>Comparison of country specific diarrhea-proportionate mortality estimates by country for the single cause-model vs. Lancet estimates <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029151#pone.0029151-Black1" target="_blank">[<b>5</b>]</a>.</p

Systematic review and data abstraction flow chart.

<p>Systematic review and data abstraction flow chart.</p

Geographic distribution of model input data and countries for which the model predicts proportionate mortality.

<p>Geographic distribution of model input data and countries for which the model predicts proportionate mortality.</p

2008 Diarrhea Proportionate mortality among children 1–59 mo of age among countries^* without vital registration data compared to regional estimates from a multi-cause mixed model approach.

<p>*Countries Included in Single Cause Model: a) Algeria, Angola, Benin, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Congo, Cote d'Ivoire, Democratic Republic of the Congo, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe; b) Bolivia, Dominican Republic, Guatemala, Haiti, Honduras, Jamaica, Nicaragua, Paraguay; c) Afghanistan, Djibouti, Iraq, Morocco, Pakistan, Somalia, Sudan, Yemen; d) Azerbaijan, Georgia, Kyrgyz Republic, Tajikistan, Turkmenistan, Uzbekistan; e) Bangladesh, Bhutan, DPR Korea, India, Indonesia, Maldives, Myanmar, Nepal, Timor-Leste; f) Cambodia, China, Lao, People's Democratic Republic of Micronesia, Mongolia, Nauru, Papua New Guinea, Philippines, Samoa, Solomon Islands, Vanuatu.</p><p>**100% of deaths included in multi-disease mixed approach Multi-cause mixed methods approach. Uncertainty ranges for total number of diarrhea deaths from MC model have been previously presented <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0029151#pone.0029151-Black1" target="_blank">[5]</a>.</p

Distribution of study level diarrhea proportionate mortality by WHO region.

<p>Distribution of study level diarrhea proportionate mortality by WHO region.</p