Progress toward the sanitation component of Millennium Development Goal (MDG) Target 7c was reassessed to account for the need to protect communities and the wider population from exposure to human excreta. We classified connections to sewerage as "improved sanitation" only if the sewage was treated before discharge to the environment. Sewerage connection data was available for 167 countries in 2010; of these, 77 had published data on sewage treatment prevalence. We developed an empirical model to estimate sewage treatment prevalence for 47 additional countries. We estimate that in 2010, 40% of the global population (2.8 billion people) used improved sanitation, as opposed to the estimate of 62% (4.3 billion people) from the WHO/UNICEF Joint Monitoring Programme (JMP), and that 4.1 billion people lacked access to an improved sanitation facility. Redefining sewerage-without-treatment as "unimproved sanitation" in MDG monitoring would raise the 1990 baseline population using unimproved sanitation from 53% to 64% and the corresponding 2015 target from 27% to 32%. At the current rate of progress, we estimate a shortfall of 28 percentage points (1.9 billion people) in 2010 and a projected 27 percentage point shortfall in 2015.

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Objectives/Hypothesis: Machine learning (ML) is a type of artificial intelligence wherein a computer learns patterns and associations between variables to correctly predict outcomes. The objectives of this study were to 1) use a ML platform to identify factors important in predicting surgical complications in patients undergoing head and neck free tissue transfer, and 2) compare ML outputs to traditionally employed logistic regression models. Study Design: Retrospective cohort study. Methods: Using a dataset of 364 consecutive patients who underwent head and neck microvascular free tissue transfer at a single institution, 14 clinicopathologic characteristics were analyzed using a supervised ML algorithm of ensemble decision trees to predict surgical complications. The relative importance values of each variable in the ML analysis were then compared to logistic regression models. Results: There were 166 surgical complications, which included bleeding or hematoma in 30 patients (8.2%), fistulae in 25 patients (6.9%), and infection or dehiscence in 52 patients (14.4%). There were 59 take-backs (16.2%), and six total (1.6%) and five partial (1.4%) flap failures. ML models were able to correctly classify outcomes with an accuracy of 65% to 75%. Factors that were identified in ML analyses as most important for predicting complications included institutional experience, flap ischemia time, age, and smoking pack-years. In contrast, the significant factors most frequently identified in traditional logistic regression analyses were patient age (P = .03), flap type (P = .03), and primary site of reconstruction (P = .06). Conclusions: In this single-institution dataset, ML algorithms identified factors for predicting complications after free tissue transfer that were distinct from traditional regression models.

Abstract. Millennium Development Goal Target 7c (to halve between 1990 and 2015 the proportion of the global population without sustainable access to safe drinking water), was celebrated as achieved in 2012. However, new studies show that we may be prematurely celebrating. Access to safe drinking water may be overestimated if microbial water quality is considered. The objective of this study was to examine the relationship between microbial drinking water quality and drinking water source in the Puerto Plata region of the Dominican Republic. This study analyzed microbial drinking water quality data from 409 households in 33 communities. Results showed that 47% of improved drinking water sources were of high to very-high risk water quality, and therefore unsafe for drinking. This study provides evidence that the current estimate of safe water access may be overly optimistic, and microbial water quality data are needed to reliably assess the safety of drinking water.

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