114 research outputs found

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

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    <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

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

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    <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

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

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

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

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    <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

    Risk of Early-Onset Neonatal Infection with Maternal Infection or Colonization: A Global Systematic Review and Meta-Analysis

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    <div><p>Background</p><p>Neonatal infections cause a significant proportion of deaths in the first week of life, yet little is known about risk factors and pathways of transmission for early-onset neonatal sepsis globally. We aimed to estimate the risk of neonatal infection (excluding sexually transmitted diseases [STDs] or congenital infections) in the first seven days of life among newborns of mothers with bacterial infection or colonization during the intrapartum period.</p><p>Methods and Findings</p><p>We searched PubMed, Embase, Scopus, Web of Science, Cochrane Library, and the World Health Organization Regional Databases for studies of maternal infection, vertical transmission, and neonatal infection published from January 1, 1960 to March 30, 2013. Studies were included that reported effect measures on the risk of neonatal infection among newborns exposed to maternal infection. Random effects meta-analyses were used to pool data and calculate the odds ratio estimates of risk of infection. Eighty-three studies met the inclusion criteria. Seven studies (8.4%) were from high neonatal mortality settings. Considerable heterogeneity existed between studies given the various definitions of laboratory-confirmed and clinical signs of infection, as well as for colonization and risk factors. The odds ratio for neonatal lab-confirmed infection among newborns of mothers with lab-confirmed infection was 6.6 (95% CI 3.9–11.2). Newborns of mothers with colonization had a 9.4 (95% CI 3.1–28.5) times higher odds of lab-confirmed infection than newborns of non-colonized mothers. Newborns of mothers with risk factors for infection (defined as prelabour rupture of membranes [PROM], preterm <37 weeks PROM, and prolonged ROM) had a 2.3 (95% CI 1.0–5.4) times higher odds of infection than newborns of mothers without risk factors.</p><p>Conclusions</p><p>Neonatal infection in the first week of life is associated with maternal infection and colonization. High-quality studies, particularly from settings with high neonatal mortality, are needed to determine whether targeting treatment of maternal infections or colonization, and/or prophylactic antibiotic treatment of newborns of high risk mothers, may prevent a significant proportion of early-onset neonatal sepsis.</p><p><i>Please see later in the article for the Editors' Summary</i></p></div

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

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    <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
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