Background/objectiveGuatemala’s indigenous Maya population has one of the highest perinatal and maternal mortality rates in Latin America. In this population most births are delivered at home by traditional birth attendants (TBAs), who have limited support and linkages to public hospitals. The goal of this study was to characterize the detection of maternal and perinatal complications and rates of facility-level referral by TBAs, and to evaluate the impact of a mHealth decision support system on these rates.MethodsA pragmatic one-year feasibility trial of an mHealth decisions support system was conducted in rural Maya communities in collaboration with TBAs. TBAs were individually randomized in an unblinded fashion to either early-access or later-access to the mHealth system. TBAs in the early-access arm used the mHealth system throughout the study. TBAs in the later-access arm provided usual care until crossing over uni-directionally to the mHealth system at the study midpoint. The primary study outcome was the monthly rate of referral to facility-level care, adjusted for birth volume.ResultsForty-four TBAs were randomized, 23 to the early-access arm and 21 to the later-access arm. Outcomes were analyzed for 799 pregnancies (early-access 425, later-access 374). Monthly referral rates to facility-level care were significantly higher among the early-access arm (median 33 referrals per 100 births, IQR 22–58) compared to the later-access arm (median 20 per 100, IQR 0–30) (p = 0.03). At the study midpoint, the later-access arm began using the mHealth platform and its referral rates increased (median 34 referrals per 100 births, IQR 5–50) with no significant difference from the early-access arm (p = 0.58). Rates of complications were similar in both arms, except for hypertensive disorders of pregnancy, which were significantly higher among TBAs in the early-access arm (RR 3.3, 95% CI 1.10–9.86).ConclusionsReferral rates were higher when TBAs had access to the mHealth platform. The introduction of mHealth supportive technologies for TBAs is feasible and can improve detection of complications and timely referral to facility-care within challenging healthcare delivery contexts.Trial registrationClinicaltrials.gov NCT02348840.

No abstract

During pregnancy, fetal cardiac monitoring is a common method for identifying fetal abnormalities in the second and third gestational trimesters (Sandmire and DeMott 1998). This identification process is performed by examining fetal heart rate (FHR) variations in signals between 10-60 min, using epochs of 3.75 s as is described in the Dawes/Redman system (Dawes et al 1981, Pardey et al 2002. Based on the observable variations,

One dimensional Doppler Ultrasound (DUS) is a low cost method for fetal auscultation. However, accuracy of any metrics derived from the DUS signals depends on their quality, which relies heavily on operator skills. In low resource settings, where skill levels are sparse, it is important for the device to provide real time signal quality feedback to allow the re-recording of data. Retrospectively, signal quality assessment can help remove low quality recordings when processing large amounts of data. To this end, we proposed a novel template-based method, to assess DUS signal quality. Data used in this study were collected from 17 pregnant women using a low-cost transducer connected to a smart phone. Recordings were split into 1990 segments of 3.75 s duration, and hand labeled for quality by three independent annotators. The proposed template-based method uses Empirical Mode Decomposition (EMD) to allow detection of the fetal heart beats and segmentation into short, time-aligned temporal windows. Templates were derived for each 15 s window of the recordings. The DUS signal quality index (SQI) was calculated by correlating the segments in each window with the corresponding running template using four different pre-processing steps: (i) no additional preprocessing, (ii) linear resampling of each beat, (iii) dynamic time warping (DTW) of each beat and (iv) weighted DTW of each beat. The template-based SQIs were combined with additional features based on sample entropy and power spectral density. To assess the performance of the method, the dataset was split into training and test subsets. The training set was used to obtain the best combination of features for predicting the DUS quality using cross validation, and the test set was used to estimate the classification accuracy using bootstrap resampling. A median out of sample classification accuracy on the test set of 85.8% was found using three features; template-based SQI, sample entropy and the relative power in the 160 to 660 Hz range. The results suggest that the new automated method can reliably assess the DUS quality, thereby helping users to consistently record DUS signals with acceptable quality for fetal monitoring.

No abstract

The demonstrations in Syria in 2011 became an uncompromising conflict that divided the country into three main areas of control: governmental areas, northeast Syria, and Northwest Syria. A series of United Nations resolutions adopted in 2014 authorizing official cross-border humanitarian aid in opposition-held areas to allow humanitarian agencies and organizations to use routes across the border from neighborhood countries like Turkey to deliver humanitarian assistance to people in need in Syria. The resolution was extended annually until 2021 when it was adapted to involve a cross-line humanitarian response from governmental areas besides cross-border operations. The last adaptation of the cross-border resolution, whose original form was interpreted as a politicized action by Russia and China, implicates an unframed and unplanned transition from an emergency to an Early Recovery status. Without an appropriate framework for the current geopolitical complexity in Syria, Early Recovery programs are doomed to fail, resulting in further complications in the political and humanitarian scenes. Moreover, the effectiveness of the cross-line mechanism is questionable, considering the lack of accessibility and acceptability for Damascus-based humanitarian operations in areas out of government control. The article reviews studies about Early Recovery guidelines and operational frameworks of health systems recovery in post-conflict settings to derive a practical and hybrid framework for operationalizing health system recovery in Northwest Syria, considering current geopolitical and humanitarian circumstances. This article draws upon the six building blocks of the health system, the essential package of public health services, Early Recovery integration criteria, health system resilience dimensions in the literature, and public health determinants to identify context-specific health system recovery challenges and priorities. As a result, we introduce a new health system recovery framework, which is operationalized for the context of Northwest Syria.

The war in Kosovo in 1999 resulted in the displacement of up to 1.5 million persons from their homes. On the subsequent return of the refugees and internally displaced persons, one of the major challenges facing the local population and the international community, was the rehabilitation of Kosovo's public health infrastructure, which had sustained enormous damage as a result of the fighting. Of particular importance was the need to develop a system of epidemic prevention and preparedness. But no single agency had the resources or capacity to implement such a program. Therefore, a unique six-point model was developed as a collaboration between the Kosovo Institute of Public Health, the World Health Organization, and an international, nongovernmental organization. Important components of the program included a major Kosovo-wide baseline health survey, the development of a provincewide public health surveillance system, rehabilitation of microbiology laboratories, and the development of a local capacity for epidemic response. While all program objectives were met, important lessons were learned concerning the planning, design, and implementation of such a project. This program represents a model that potentially could be replicated in other post-conflict or development settings.

Objectives Unhealthy food marketing to children adversely affects their diet quality and health. The negative impacts of this marketing may be amplified on digital media, which allows industry to use artificial intelligence (AI) to market unhealthy food to children in covert ways. Health Canada is developing regulations to prohibit digital marketing of unhealthy food that appeals to children <13 years. However, reliance on adults to manually assess food marketing to children on digital media has limited understanding of key targets for policy and capacity to monitor policy adherence. To address these gaps, we are developing an AI system to monitor marketing of unhealthy food to children on digital media, including websites, YouTube, social media and mobile gaming apps. Methods Our web and mobile scrapers continuously collect marketing instances that may be viewed by individuals in Canada on websites and social media applications popular with children. This has allowed us to accumulate a database of > 615,000 marketing instances. The AI system extracts features from each marketing instance to determine whether foods are present, and if so, whether they are unhealthy according to Health Canada's standards (based on the presence of added saturated fat, added sodium and/or free sugars). Next, the AI system uses a supervised machine learning model to assess whether child appealing marketing techniques are present. In the final step, the system integrates all of the data collected to determine whether a given marketing instance features unhealthy foods and appeals to children. The system can be applied to monitor the extent and nature of digital food marketing to children internationally. It can also be retrained to monitor adherence to country-specific policy. Results This is a protocol paper so there are no results. Conclusions The AI system provides a scalable, objective and reproducible manner to identify digital marketing of unhealthy food that appeals to children across the digital marketing landscape. The system can assist researchers and policy makers to study children's exposure to digital marketing of unhealthy food and its impacts, and to monitor adherence to policy that restricts this marketing. Funding Sources Canadian Institutes of Health Research.