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Abstract Background In 2014, Vietnam was the first Southeast Asian country to commit to achieving the World Health Organization’s 90–90-90 global HIV targets (90% know their HIV status, 90% on sustained treatment, and 90% virally suppressed) by 2020. This pledge represented further confirmation of Vietnam’s efforts to respond to the HIV epidemic, one feature of which has been close collaboration with the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR). Starting in 2004, PEPFAR supported community outreach programs targeting high-risk populations (people who inject drugs, men who have sex with men, and sex workers). To provide early evidence on program impact, in 2007–2008 we conducted a nationwide evaluation of PEPFAR-supported outreach programs in Vietnam. The evaluation focused on assessing program effect on HIV knowledge, high-risk behaviors, and HIV testing among high-risk populations—results relevant to Vietnam’s push to meet global HIV goals. Methods We used a mixed-methods cross-sectional evaluation design. Data collection encompassed a quantitative survey of 2199 individuals, supplemented by 125 in-depth interviews. Participants were members of high-risk populations who reported recent contact with an outreach worker (intervention group) or no recent contact (comparison group). We assessed differences in HIV knowledge, risky behaviors, and HIV testing between groups, and between high-risk populations. Results Intervention participants knew significantly more about transmission, prevention, and treatment than comparison participants. We found low levels of injection drug-use-related risk behaviors and little evidence of program impact on such behaviors. In contrast, a significantly smaller proportion of intervention than comparison participants reported risky sexual behaviors generally and within each high-risk population. Intervention participants were also more likely to have undergone HIV testing (76.1% vs. 47.0%, p

Abstract Background There are few published standards or methodological guidelines for integrating Data Quality Assurance (DQA) protocols into large-scale health systems research trials, especially in resource-limited settings. The BetterBirth Trial is a matched-pair, cluster-randomized controlled trial (RCT) of the BetterBirth Program, which seeks to improve quality of facility-based deliveries and reduce 7-day maternal and neonatal mortality and maternal morbidity in Uttar Pradesh, India. In the trial, over 6300 deliveries were observed and over 153,000 mother-baby pairs across 120 study sites were followed to assess health outcomes. We designed and implemented a robust and integrated DQA system to sustain high-quality data throughout the trial. Methods We designed the Data Quality Monitoring and Improvement System (DQMIS) to reinforce six dimensions of data quality: accuracy, reliability, timeliness, completeness, precision, and integrity. The DQMIS was comprised of five functional components: 1) a monitoring and evaluation team to support the system; 2) a DQA protocol, including data collection audits and targets, rapid data feedback, and supportive supervision; 3) training; 4) standard operating procedures for data collection; and 5) an electronic data collection and reporting system. Routine audits by supervisors included double data entry, simultaneous delivery observations, and review of recorded calls to patients. Data feedback reports identified errors automatically, facilitating supportive supervision through a continuous quality improvement model. Results The five functional components of the DQMIS successfully reinforced data reliability, timeliness, completeness, precision, and integrity. The DQMIS also resulted in 98.33% accuracy across all data collection activities in the trial. All data collection activities demonstrated improvement in accuracy throughout implementation. Data collectors demonstrated a statistically significant (p = 0.0004) increase in accuracy throughout consecutive audits. The DQMIS was successful, despite an increase from 20 to 130 data collectors. Conclusions In the absence of widely disseminated data quality methods and standards for large RCT interventions in limited-resource settings, we developed an integrated DQA system, combining auditing, rapid data feedback, and supportive supervision, which ensured high-quality data and could serve as a model for future health systems research trials. Future efforts should focus on standardization of DQA processes for health systems research. Trial Registration ClinicalTrials.gov identifier, NCT02148952 . Registered on 13 February 2014.

Abstract Background Residential accommodation for expectant mothers adjacent to health facilities, known as maternity waiting homes (MWH), is an intervention designed to improve access to skilled deliveries in low-income countries like Zambia where the maternal mortality ratio is estimated at 398 deaths per 100,000 live births. Our study aimed to assess the relationship between MWH quality and the likelihood of facility delivery in Kalomo and Choma Districts in Southern Province, Zambia. Methods We systematically assessed and inventoried the functional capacity of all existing MWH using a quantitative facility survey and photographs of the structures. We calculated a composite score and used multivariate regression to quantify MWH quality and its association with the likelihood of facility delivery using household survey data collected on delivery location in Kalomo and Choma Districts from 2011–2013. Results MWH were generally in poor condition and composite scores varied widely, with a median score of 28.0 and ranging from 12 to 66 out of a possible 75 points. Of the 17,200 total deliveries captured from 2011–2013 in 40 study catchment area facilities, a higher proportion occurred in facilities where there was either a MWH or the health facility provided space for pregnant waiting mothers compared to those with no accommodations (60.7% versus 55.9%, p