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Oral cholera vaccines (OCVs) are relatively new public health interventions, and limited data exist on the potential impact of OCV use on traditional cholera prevention and control measures—safe water, sanitation and hygiene (WaSH). To assess OCV acceptability and knowledge, attitudes, and practices (KAPs) regarding cholera and WaSH, we conducted cross-sectional surveys, 1 month before (baseline) and 3 and 12 months after (first and second follow-up) a preemptive OCV campaign in Maela, a long-standing refugee camp on the Thailand-Burma border. We randomly selected households for the surveys, and administered questionnaires to female heads of households. In total, 271 (77%), 187 (81%), and 199 (85%) households were included in the baseline, first and second follow-up surveys, respectively. Anticipated OCV acceptability was 97% at baseline, and 91% and 85% of household members were reported to have received 1 and 2 OCV doses at first follow-up. Compared with baseline, statistically significant differences (95% Wald confidence interval not overlapping zero) were noted at first and second follow-up among the proportions of respondents who correctly identified two or more means of cholera prevention (62% versus 78% and 80%), reported boiling or treating drinking water (19% versus 44% and 69%), and washing hands with soap (66% versus 77% and 85%); a significant difference was also observed in the proportion of households with soap available at handwashing areas (84% versus 90% and 95%), consistent with reported behaviors. No significant difference was noted in the proportion of households testing positive for Escherichia coli in stored household drinking water at second follow-up (39% versus 49% and 34%). Overall, we observed some positive, and no negative changes in cholera- and WaSH-related KAPs after an OCV campaign in Maela refugee camp. OCV campaigns may provide opportunities to reinforce beneficial WaSH-related KAPs for comprehensive cholera prevention and control.

Ensuring access to and promoting use of effective contraception have been identified as important strategies for preventing unintended pregnancy (1). The importance of ensuring resources to prevent unintended pregnancy in the context of public health emergencies was highlighted during the 2016 Zika virus outbreak when Zika virus infection during pregnancy was identified as a cause of serious birth defects (2). Accordingly, CDC outlined strategies for state, local, and territorial jurisdictions to consider implementing to ensure access to contraception (3). To update previously published contraceptive use estimates* among women at risk for unintended pregnancy† and to estimate the number of women with ongoing or potential need for contraceptive services,§,¶ data on contraceptive use were collected during September–December 2016 through the Behavioral Risk Factor Surveillance System (BRFSS). Results from 21 jurisdictions indicated that most women aged 18–49 years were at risk for unintended pregnancy (range across jurisdictions = 57.4%–76.8%). Estimates of the number of women with ongoing or potential need for contraceptive services ranged from 368 to 617 per 1,000 women aged 18–49 years. The percentage of women at risk for unintended pregnancy using a most or moderately effective contraceptive method** ranged from 26.1% to 65.7%. Jurisdictions can use this information to estimate the number of women who might seek contraceptive services and to plan and evaluate efforts to increase contraceptive use. This information is particularly important in the context of public health emergencies, such as the recent Zika virus outbreak, which have been associated with increased risk for adverse maternal-infant outcomes (2,4–6) and have highlighted the importance of providing women and their partners with resources to prevent unintended pregnancy.

In 2016, Centers for Disease Control and Prevention (CDC) established surveillance of pregnant women with Zika virus infection and their infants in the U.S. states, territories, and freely associated states. To identify cases of Zikaassociated birth defects, subject matter experts review data reported from medical records of completed pregnancies to identify findings that meet surveillance case criteria (manual review). The volume of reported data increased over the course of the Zika virus outbreak in the Americas, challenging the resources of the surveillance system to conduct manual review. Machine learning was explored as a possible method for predicting case status. Ensemble models (using machine learning algorithms including support vector machines, logistic regression, random forests, k-nearest neighbors, gradient boosted trees, and decision trees) were developed and trained using data collected from January 2016-October 2017. Models were developed separately, on data from the U.S. states, non-Puerto Rico territories, and freely associated states (referred to as the U.S. Zika Pregnancy and Infant Registry [USZPIR]) and data from Puerto Rico (referred to as the Zika Active Pregnancy Surveillance System [ZAPSS]) due to differences in data collection and storage methods. The machine learning models demonstrated high sensitivity for identifying cases while potentially reducing volume of data for manual review (USZPIR: 96% sensitivity, 25% reduction in review volume; ZAPSS: 97% sensitivity, 50% reduction in review volume). Machine learning models show potential for identifying cases of Zika-associated birth defects and for reducing volume of data for manual review, a potential benefit in other public health emergency response settings.

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