The cascading impacts of attacks on health in Syria: A qualitative study of health system and community impacts.

Syria has experienced over a decade of armed conflict, characterized by targeted violence against healthcare. The impacts of these attacks have resulted in both direct and indirect attacks on health and reverberating effects on local communities. This study aims to explore the perspectives of health workers based in northern Syria who have experienced such attacks on health to understand the impacts on the health system as well as communities served. In-depth interviews were conducted with health workers in the northern regions of Syria where attacks on health have been frequent. Participants were identified using purposive and snowball sampling. Interviews were coded and analyzed using the Framework Method. Our inductive and deductive codes aligned closely with the WHO Health System Building Blocks framework, and we therefore integrated this framing into the presentation of findings. We actively sought to include female and non-physician health workers as both groups have been under-represented in previous research in northern Syria. A total of 40 health workers (32.5% female, 77.5% non-physicians) who experienced attacks in northern Syria between 2013 and 2020 participated in interviews in 2020-2021. Participants characterized attacks on health as frequent, persistent over years, and strategically targeted. The attacks had both direct and indirect impacts on the health system and consequently the wider health of the community. For the health system, participants noted compounded impacts on the delivery of care, health system governance, and challenges to financing, workforce, and infrastructure. Reconstructing health facilities or planning services in the aftermath of attacks on health was challenging due to poor health system governance and resource challenges. These impacts had ripple effects on the health of the community, particularly the most vulnerable. The impacts of attacks on health in Syria are multiple, with both short- and long-term consequences for the health system(s) across Syria as well as the health of communities in these respective areas. Though such attacks against healthcare are illegal under international humanitarian law, this and other legal frameworks have led to little accountability in the face of such attacks both in Syria and elsewhere. Characterizing their impacts is essential to improving our understanding of the consequences of attacks as a public health issue and supporting protection and advocacy efforts.

Cluster of Ebola Virus Disease, Bong and Montserrado Counties, Liberia.

Lack of trust in government-supported services after the death of a health care worker with symptoms of Ebola resulted in ongoing Ebola transmission in 2 Liberia counties. Ebola transmission was facilitated by attempts to avoid cremation of the deceased patient and delays in identifying and monitoring contacts.

Derivation and Internal Validation of the Ebola Prediction Score for Risk Stratification of Patients With Suspected Ebola Virus Disease.

STUDY OBJECTIVE: The current outbreak of Ebola virus disease in West Africa is the largest on record and has overwhelmed the capacity of local health systems and the international community to provide sufficient isolation and treatment of all suspected cases. The goal of this study is to develop a clinical prediction model that can help clinicians risk-stratify patients with suspected Ebola virus disease in the context of such an epidemic. METHODS: A retrospective analysis was performed of patient data collected during routine clinical care at the Bong County Ebola Treatment Unit in Liberia during its first 16 weeks of operation. The predictive power of 14 clinical and epidemiologic variables was measured against the primary outcome of laboratory-confirmed Ebola virus disease, using logistic regression to develop a final prediction model. Bootstrap sampling was used to assess the internal validity of the model and estimate its performance in a simulated validation cohort. RESULTS: Ebola virus disease testing results were available for 382 (97%) of 395 patients admitted to the Ebola treatment unit during the study period. A total of 160 patients (42%) tested positive for Ebola virus disease. Logistic regression analysis identified 6 variables independently predictive of laboratory-confirmed Ebola virus disease, including sick contact, diarrhea, loss of appetite, muscle pains, difficulty swallowing, and absence of abdominal pain. The Ebola Prediction Score, constructed with these 6 variables, had an area under the receiver operator characteristic curve of 0.75 (95% confidence interval 0.70 to 0.80) for the prediction of laboratory-confirmed Ebola virus disease. Patients with higher Ebola Prediction Scores had higher likelihoods of laboratory-confirmed Ebola virus disease. CONCLUSION: The Ebola Prediction Score can be used by clinicians as an adjunct to current Ebola virus disease case definitions to risk-stratify patients with suspected Ebola virus disease. Clinicians can use this new tool for the purpose of cohorting patients within the suspected-disease ward of an Ebola treatment unit or community-based isolation center to prevent nosocomial infection or as a triage tool when patient numbers overwhelm available capacity. Given the inherent limitations of clinical prediction models, however, a low-cost, point-of-care test that can rapidly and definitively exclude Ebola virus disease in patients should be a research priority.

Capitalizing on the characteristics of mHealth to evaluate its impact.

The field of mHealth has made significant advances in a short period of time, demanding a more thorough and scientific approach to understanding and evaluating its progress. A recent review of mHealth literature identified two primary research needs in order for mHealth to strengthen health systems and promote healthy behaviors, namely health outcomes and cost-benefits (Mechael et al., 2010 ). In direct response to the gaps identified in mHealth research, the aim of this paper is to present the study design and highlight key observations and next steps from an evaluation of the mHealth activities within the electronic health (eHealth) architecture implemented by the Millennium Villages Project (MVP) by leveraging data generated through mobile technology itself alongside complementary qualitative research and costing assessments. The study, funded by the International Development and Research Centre (IDRC) as part of the Open Architecture Standards and Information Systems research project (OASIS II) (Sinha, 2009 ), is being implemented on data generated by 14 MVP sites in 10 Sub-Saharan African countries including more in-depth research in Ghana, Rwanda, Tanzania, and Uganda. Specific components of the study include rigorous quantitative case-control analyses and other epidemiological approaches (such as survival analysis) supplemented by in-depth qualitative interviews spread out over 18 months, as well as a costing study to assess the impact of mHealth on health outcomes, service delivery, and efficiency.