The emergence of mobile technology offers new opportunities to improve clinical guideline adherence in resource-limited settings. We conducted a clinical pilot study in rural Bangladesh to evaluate the impact of a smartphone adaptation of the World Health Organization (WHO) diarrheal disease management guidelines, including a modality for age-based weight estimation. Software development was guided by end-user input and evaluated in a resource-limited district and sub-district hospital during the fall 2015 cholera season; both hospitals lacked scales which necessitated weight estimation. The study consisted of a 6 week pre-intervention and 6 week intervention period with a 10-day post-discharge follow-up. Standard of care was maintained throughout the study with the exception that admitting clinicians used the tool during the intervention. Inclusion criteria were patients two months of age and older with uncomplicated diarrheal disease. The primary outcome was adherence to guidelines for prescriptions of intravenous (IV) fluids, antibiotics and zinc. A total of 841 patients were enrolled (325 pre-intervention; 516 intervention). During the intervention, the proportion of prescriptions for IV fluids decreased at the district and sub-district hospitals (both p < 0.001) with risk ratios (RRs) of 0.5 and 0.2, respectively. However, when IV fluids were prescribed, the volume better adhered to recommendations. The proportion of prescriptions for the recommended antibiotic azithromycin increased (p < 0.001 district; p = 0.035 sub-district) with RRs of 6.9 (district) and 1.6 (sub-district) while prescriptions for other antibiotics decreased; zinc adherence increased. Limitations included an absence of a concurrent control group and no independent dehydration assessment during the pre-intervention. Despite limitations, opportunities were identified to improve clinical care, including better assessment, weight estimation, and fluid/ antibiotic selection. These findings demonstrate that a smartphone-based tool can improve guideline adherence. This study should serve as a catalyst for a randomized controlled trial to expand on the findings and address limitations.

Background Acute diarrhoeal disease management often requires rehydration alone without antibiotics. However, non-indicated antibiotics are frequently ordered and this is an important driver of antimicrobial resistance. The mHealth Diarrhoea Management (mHDM) trial aimed to establish whether electronic decision support improves rehydration and antibiotic guideline adherence in resource-limited settings.Methods A cluster randomised controlled trial was done at ten district hospitals in Bangladesh. Inclusion criteria were patients aged 2 months or older with uncomplicated acute diarrhoea. Admission orders were observed without intervention in the pre-intervention period, followed by randomisation to electronic (rehydration calculator) or paper formatted WHO guidelines for the intervention period. The primary outcome was rate of intravenous fluid ordered as a binary variable. Generalised linear mixed-effect models, accounting for hospital clustering, served as the analytical framework; the analysis was intention to treat. The trial is registered with ClinicalTrials.gov (NCT03154229) and is completed. Findings From March 11 to Sept 10, 2018, 4975 patients (75•6%) of 6577 screened patients were enrolled. The intervention effect for the primary outcome showed no significant differences in rates of intravenous fluids ordered as a function of decision-support type. Intravenous fluid orders decreased by 0•9 percentage points for paper electronic decision support and 4•2 percentage points for electronic decision support, with a 4•2-point difference between decision-support types in the intervention period (paper 98•7% [95% CI 91•8-99•8] vs electronic 94•5% [72•2-99•1]; p interaction =0•31). Adverse events such as complications and mortality events were uncommon and could not be statistically estimated. Interpretation Although intravenous fluid orders did not change, electronic decision support was associated with increases in the volume of intravenous fluid ordered and decreases in antibiotics ordered, which are consistent with WHO guidelines.

. Nomadic pastoralists are among the world’s hardest-to-reach and least served populations. Pastoralist communities are difficult to capture in household surveys because of factors including their high degree of mobility over remote terrain, fluid domestic arrangements, and cultural barriers. Most surveys use census-based sampling frames which do not accurately capture the demographic and health parameters of nomadic populations. As a result, pastoralists are “invisible” in population data such as the Demographic and Health Surveys (DHS). By combining remote sensing and geospatial analysis, we developed a sampling strategy designed to capture the current distribution of nomadic populations. We then implemented this sampling frame to survey a population of mobile pastoralists in southwest Ethiopia, focusing on maternal and child health (MCH) indicators. Using standardized instruments from DHS questionnaires, we draw comparisons with regional and national data finding disparities with DHS data in core MCH indicators, including vaccination coverage, skilled birth attendance, and nutritional status. Our field validation demonstrates that this method is a logistically feasible alternative to conventional sampling frames and may be used at the population level. Geospatial sampling methods provide cost-affordable and logistically feasible strategies for sampling mobile populations, a crucial first step toward reaching these groups with health services.

ObjectiveTo identify determinants of intended versus actual care-seeking behaviours in a pluralistic healthcare system that is reliant on both conventional and non-conventional providers and discover opportunities to catalyse improved healthcare access.DesignCross-sectional study.Setting and participantsIn Haiti 568 households (incorporating 2900 members) with children less than 5 years of age were randomly sampled geographically with stratifications for population density. These households identified the healthcare providers they frequented. Among 140 providers, 65 were located and enrolled.Outcome measuresHousehold questionnaires with standardised cases (intentions) were compared with self-recall of health events (behaviours). The connectedness of households and their providers was determined by network analysis.ResultsHouseholds reported 636 health events in the prior month. Households sought care for 35% (n=220) and treated with home remedies for 44% (n=277). The odds of seeking care increased 217% for severe events (adjusted OR (aOR)=3.17; 95% CI 1.99 to 5.05; p<0.001). The odds of seeking care from a conventional provider increased by 37% with increasing distance (aOR=1.37; 95% CI 1.06 to 1.79; p=0.016). Despite stating an intention to seek care from conventional providers, there was a lack of congruence in practice that favoured non-conventional providers (McNemar’s χ2 test p<0.001). Care was sought from primary providers for 68% (n=150) of cases within a three-tiered network; 25% (n=38/150) were non-conventional.ConclusionAddressing geographic barriers, possibly with technology solutions, should be prioritised to meet healthcare seeking intentions while developing approaches to connect non-conventional providers into healthcare networks when geographic barriers cannot be overcome.