Cloud-Based Influenza Surveillance System in Emergency Departments Using Molecular-Based Testing: Advances and Challenges.

INTRODUCTION: Electronic influenza surveillance systems aid in health surveillance and clinical decision-making within the emergency department (ED). While major advances have been made in integrating clinical decision-making tools within the electronic health record (EHR), tools for sharing surveillance data are often piecemeal, with the need for data downloads and manual uploads to shared servers, delaying time from data acquisition to end-user. Real-time surveillance can help both clinicians and public health professionals recognize circulating influenza earlier in the season and provide ongoing situational awareness. METHODS: We created a prototype, cloud-based, real-time reporting system in two large, academically affiliated EDs that streamed continuous data on a web-based dashboard within hours of specimen collection during the influenza season. Data included influenza test results (positive or negative) coupled with test date, test instrument geolocation, and basic patient demographics. The system provided immediate reporting to frontline clinicians and to local, state, and federal health department partners. RESULTS: We describe the process, infrastructure requirements, and challenges of developing and implementing the prototype system. Key process-related requirements for system development included merging data from the molecular test (GeneXpert) with the hospitals' EHRs, securing data, authorizing/authenticating users, and providing permissions for data access refining visualizations for end-users. CONCLUSION: In this case study, we effectively integrated multiple data systems at four distinct hospital EDs, relaying data in near real time to hospital-based staff and local and national public health entities, to provide laboratory-confirmed influenza test results during the 2014-2015 influenza season. Future innovations need to focus on integrating the dashboard within the EHR and clinical decision tools.

A Community Health Worker Intervention to Increase Childhood Disease Treatment Coverage in Rural Liberia: A Controlled Before-and-After Evaluation.

OBJECTIVES: To assess a community health worker (CHW) program's impact on childhood illness treatment in rural Liberia. METHODS: We deployed CHWs in half of Rivercess County in August 2015 with the other half constituting a comparison group until July 2016. All CHWs were provided cash incentives, supply chain support, and monthly clinical supervision. We conducted stratified cluster-sample population-based surveys at baseline (March-April 2015) and follow-up (April-June 2016) and performed a difference-in-differences analysis, adjusted by inverse probability of treatment weighting, to assess changes in treatment of fever, diarrhea, and acute respiratory infection by a qualified provider. RESULTS: We estimated a childhood treatment difference-in-differences of 56.4 percentage points (95% confidence interval [CI] = 36.4, 76.3). At follow-up, CHWs provided 57.6% (95% CI = 42.8, 71.2) of treatment in the intervention group. The difference-in-differences diarrhea oral rehydration therapy was 22.4 percentage points (95% CI = -0.7, 45.5). CONCLUSIONS: Implementation of a CHW program in Rivercess County, Liberia, was associated with large, statistically significant improvements treatment by a qualified provider; however, improvements in correct diarrhea treatment were lower than improvements in coverage. Findings from this study offer support for expansion of Liberia's new National Community Health Assistant Program.

Implementation research on community health workers' provision of maternal and child health services in rural Liberia.

OBJECTIVE: To assess changes in the use of essential maternal and child health services in Konobo, Liberia, after implementation of an enhanced community health worker (CHW) programme. METHODS: The Liberian Ministry of Health partnered with Last Mile Health, a nongovernmental organization, to implement a pilot CHW programme with enhanced recruitment, training, supervision and compensation. To assess changes in maternal and child health-care use, we conducted repeated cross-sectional cluster surveys before (2012) and after (2015) programme implementation. FINDINGS: Between 2012 and 2015, 54 CHWs, seven peer supervisors and three clinical supervisors were trained to serve a population of 12 127 people in 44 communities. The regression-adjusted percentage of children receiving care from formal care providers increased by 60.1 (95% confidence interval, CI: 51.6 to 68.7) percentage points for diarrhoea, by 30.6 (95% CI: 20.5 to 40.7) for fever and by 51.2 (95% CI: 37.9 to 64.5) for acute respiratory infection. Facility-based delivery increased by 28.2 points (95% CI: 20.3 to 36.1). Facility-based delivery and formal sector care for acute respiratory infection and diarrhoea increased more in agricultural than gold-mining communities. Receipt of one-or-more antenatal care sessions at a health facility and postnatal care within 24 hours of delivery did not change significantly. CONCLUSION: We identified significant increases in uptake of child and maternal health-care services from formal providers during the pilot CHW programme in remote rural Liberia. Clinic-based services, such as postnatal care, and services in specific settings, such as mining areas, require additional interventions to achieve optimal outcomes.

Facility-Based Delivery during the Ebola Virus Disease Epidemic in Rural Liberia: Analysis from a Cross-Sectional, Population-Based Household Survey.

BACKGROUND: The Ebola virus disease (EVD) epidemic has threatened access to basic health services through facility closures, resource diversion, and decreased demand due to community fear and distrust. While modeling studies have attempted to estimate the impact of these disruptions, no studies have yet utilized population-based survey data. METHODS AND FINDINGS: We conducted a two-stage, cluster-sample household survey in Rivercess County, Liberia, in March-April 2015, which included a maternal and reproductive health module. We constructed a retrospective cohort of births beginning 4 y before the first day of survey administration (beginning March 24, 2011). We then fit logistic regression models to estimate associations between our primary outcome, facility-based delivery (FBD), and time period, defined as the pre-EVD period (March 24, 2011-June 14, 2014) or EVD period (June 15, 2014-April 13, 2015). We fit both univariable and multivariable models, adjusted for known predictors of facility delivery, accounting for clustering using linearized standard errors. To strengthen causal inference, we also conducted stratified analyses to assess changes in FBD by whether respondents believed that health facility attendance was an EVD risk factor. A total of 1,298 women from 941 households completed the survey. Median age at the time of survey was 29 y, and over 80% had a primary education or less. There were 686 births reported in the pre-EVD period and 212 in the EVD period. The unadjusted odds ratio of facility-based delivery in the EVD period was 0.66 (95% confidence interval [CI] 0.48-0.90, p-value = 0.010). Adjustment for potential confounders did not change the observed association, either in the principal model (adjusted odds ratio [AOR] = 0.70, 95%CI 0.50-0.98, p = 0.037) or a fully adjusted model (AOR = 0.69, 95%CI 0.50-0.97, p = 0.033). The association was robust in sensitivity analyses. The reduction in FBD during the EVD period was observed among those reporting a belief that health facilities are or may be a source of Ebola transmission (AOR = 0.59, 95%CI 0.36-0.97, p = 0.038), but not those without such a belief (AOR = 0.90, 95%CI 0.59-1.37, p = 0.612). Limitations include the possibility of FBD secular trends coincident with the EVD period, recall errors, and social desirability bias. CONCLUSIONS: We detected a 30% decreased odds of FBD after the start of EVD in a rural Liberian county with relatively few cases. Because health facilities never closed in Rivercess County, this estimate may under-approximate the effect seen in the most heavily affected areas. These are the first population-based survey data to show collateral disruptions to facility-based delivery caused by the West African EVD epidemic, and they reinforce the need to consider the full spectrum of implications caused by public health emergencies.

Insulin reveals Akt signaling as a novel regulator of norepinephrine transporter trafficking and norepinephrine homeostasis.

Noradrenergic signaling in the CNS plays an essential role in circuits involving attention, mood, memory, and stress as well as providing pivotal support for autonomic function in the peripheral nervous system. The high-affinity norepinephrine (NE) transporter (NET) is the primary mechanism by which noradrenergic synaptic transmission is terminated. Data indicate that NET function is regulated by insulin, a hormone critical for the regulation of metabolism. Given the high comorbidity of metabolic disorders such as diabetes and obesity with mental disorders such as depression and schizophrenia, we sought to determine how insulin signaling regulates NET function and thus noradrenergic homeostasis. Here, we show that acute insulin treatment, through the downstream kinase protein kinase B (Akt), significantly decreases NET surface expression in mouse hippocampal slices and superior cervical ganglion neuron boutons (sites of synaptic NE release). In vivo manipulation of insulin/Akt signaling, with streptozotocin, a drug that induces a type 1-like diabetic state in mice, also results in aberrant NET function and NE homeostasis. Notably, we also demonstrate that Akt inhibition or stimulation, independent of insulin, is capable of altering NET surface availability. These data suggest that aberrant states of Akt signaling such as in diabetes and obesity have the potential to alter NET function and noradrenergic tone in the brain. Furthermore, they provide one potential molecular mechanism by which Akt, a candidate gene for mood disorders such as schizophrenia and depression, can impact brain monoamine homeostasis.