Mortality among populations affected by armed conflict in northeast Nigeria, 2016 to 2019.

Armed conflict, displacement and food insecurity have affected Adamawa, Borno, and Yobe states of northeast Nigeria (population ≈ 12 million) since 2009. Insecurity escalated in 2013 to 2015, but the humanitarian response was delayed and the crisis' health impact was unquantified due to incomplete death registration and limited ground access. We estimated mortality attributable to this crisis using a small-area estimation approach that circumvented these challenges. We fitted a mixed effects model to household mortality data collected as part of 70 ground surveys implemented by humanitarian actors. Model predictors, drawn from existing data, included livelihood typology, staple cereal price, vaccination geocoverage, and humanitarian actor presence. To project accurate death tolls, we reconstructed population denominators based on forced displacement. We used the model and population estimates to project mortality under observed conditions and varying assumed counterfactual conditions, had there been no crisis, with the difference providing excess mortality. Death rates were highly elevated across most ground surveys, with net negative household migration. Between April 2016 and December 2019, we projected 490,000 excess deaths (230,000 children under 5 y) in the most likely counterfactual scenario, with a range from 90,000 (best-case) to 550,000 (worst-case). Death rates were two to three times higher than counterfactual levels, double the projected national rate, and highest in 2016 to 2017. Despite limited scope (we could not study the situation before 2016 or in neighboring affected countries), our findings suggest a staggering health impact of this crisis. Further studies to document mortality in this and other crises are needed to guide decision-making and memorialize their human toll.

Population mortality before and during the COVID-19 epidemic in two Sudanese settings: a key informant study.

BACKGROUND: Population mortality is an important metric that sums information from different public health risk factors into a single indicator of health. However, the impact of COVID-19 on population mortality in low-income and crisis-affected countries like Sudan remains difficult to measure. Using a community-led approach, we estimated excess mortality during the COVID-19 epidemic in two Sudanese communities. METHODS: Three sets of key informants in two study locations, identified by community-based research teams, were administered a standardised questionnaire to list all known decedents from January 2017 to February 2021. Based on key variables, we linked the records before analysing the data using a capture-recapture statistical technique that models the overlap among lists to estimate the true number of deaths. RESULTS: We estimated that deaths per day were 5.5 times higher between March 2020 and February 2021 compared to the pre-pandemic period in East Gezira, while in El Obeid City, the rate was 1.6 times higher. CONCLUSION: This study suggests that using a community-led capture-recapture methodology to measure excess mortality is a feasible approach in Sudan and similar settings. Deploying similar community-led estimation methodologies should be considered wherever crises and weak health infrastructure prevent an accurate and timely real-time understanding of epidemics' mortality impact in real-time.

Performance of a safe and dignified burial intervention during an Ebola epidemic in the eastern Democratic Republic of the Congo, 2018-2019.

BACKGROUND: A protracted Ebola Virus Disease (EVD) epidemic in the eastern Ituri, North and South Kivu provinces of the Democratic Republic of Congo (DRC) caused 3470 confirmed and probable cases between July 2018 and April 2020. During the epidemic, the International Federation of Red Cross and Red Crescent Societies (IFRC) supported the DRC Red Cross and other local actors to offer safe and dignified burials (SDB) for suspected and confirmed EVD cases, so as to reduce transmission associated with infectious dead bodies. We conducted a retrospective cohort study of the SDB service's performance in order to inform future applications of this intervention. METHODS: We analysed data on individual SDB responses to quantify performance based on key indicators and against pre-specified service standards. Specifically, we defined SDB timeliness as response within 24 h and success as all components of the service being implemented. Combining the database with other information sources, we also fit generalised linear mixed binomial models to explore factors associated with unsuccessful SDB. RESULTS: Out of 14,624 requests for SDB, 99% were responded to, 89% within 24 h. Overall, 61% of SDBs were successful, somewhat below target (80%), with failures clustered during a high-insecurity period. Factors associated with increased odds of unsuccessful SDB included reported community and/or family nonacceptance, insecurity and suspensions of the EVD response, low health facility coverage and high coverage of radio and telephony. Burials supported by mobile Civil Protection (local authorities) and/or static, community-based 'harm reduction' teams were associated with lower odds of failure. CONCLUSIONS: A large-scale, timely and moderately performant SDB service proved feasible during the challenging eastern DRC EVD response. Burial teams that are managed by community actors and operate locally, and supported rather than owned by the Red Cross or other humanitarian organisations, are a promising modality of delivering this pillar of EVD control.

Design and implementation of a web-based, respondent-driven sampling solution.

BACKGROUND: Respondent-driven sampling (RDS) refers both to a chain-referral sampling method and an analytical model for analysing sampled data. Web-based respondent-driven sampling (webRDS) uses internet-based recruitment coupled with an electronic survey to carry out RDS studies; there is currently no commercially available webRDS solution. We designed and developed a webRDS solution to support a research study aimed at estimating conflict-attributable mortality in Yemen. Our webRDS solution is composed of an existing survey platform (i.e. ODK) and a bespoke RDS system. The RDS system is designed to administer and manage an RDS survey cascade and includes: (1) an application programming interface, (2) a study participant client, and (3) an administrator interface. We report here on the design of the webRDS solution and its implementation. RESULTS: We consulted members of the Yemeni diaspora throughout the development of the solution. Technical obstacles were largely the result of: WhatsApp's policies on bulk messaging and automated messaging behaviour, the inherent constraints of SMS messaging, and SMS filtering behaviour. Language support was straight-forward yet time consuming. Survey uptake was lower than expected. Factors which may have impacted uptake include: our use of consumable survey links, low interest amongst the diaspora population, lack of material incentives, and the length and subject matter of the survey itself. The SMS/WhatsApp messaging integration was relatively complex and limited the information we could send potential participants. CONCLUSION: Despite lower-than expected survey uptake we believe our webRDS solution provides efficient and flexible means to survey a globally diverse population.

A method for small-area estimation of population mortality in settings affected by crises.

BACKGROUND: Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and conflict resolution. METHODS: We describe here a 'small-area estimation' method to circumvent these data gaps and quantify both total and excess (i.e. crisis-attributable) death rates and tolls, both overall and for granular geographic (e.g. district) and time (e.g. month) strata. The method is based on analysis of data previously collected by national and humanitarian actors, including ground survey observations of mortality, displacement-adjusted population denominators and datasets of variables that may predict the death rate. We describe the six sequential steps required for the method's implementation and illustrate its recent application in Somalia, South Sudan and northeast Nigeria, based on a generic set of analysis scripts. RESULTS: Descriptive analysis of ground survey data reveals informative patterns, e.g. concerning the contribution of injuries to overall mortality, or household net migration. Despite some data sparsity, for each crisis that we have applied the method to thus far, available predictor data allow the specification of reasonably predictive mixed effects models of crude and under 5 years death rate, validated using cross-validation. Assumptions about values of the predictors in the absence of a crisis provide counterfactual and excess mortality estimates. CONCLUSIONS: The method enables retrospective estimation of crisis-attributable mortality with considerable geographic and period stratification, and can therefore contribute to better understanding and historical memorialisation of the public health effects of crises. We discuss key limitations and areas for further development.

The practice of evaluating epidemic response in humanitarian and low-income settings: a systematic review.

BACKGROUND: Epidemics of infectious disease occur frequently in low-income and humanitarian settings and pose a serious threat to populations. However, relatively little is known about responses to these epidemics. Robust evaluations can generate evidence on response efforts and inform future improvements. This systematic review aimed to (i) identify epidemics reported in low-income and crisis settings, (ii) determine the frequency with which evaluations of responses to these epidemics were conducted, (iii) describe the main typologies of evaluations undertaken and (iv) identify key gaps and strengths of recent evaluation practice. METHODS: Reported epidemics were extracted from the following sources: World Health Organization Disease Outbreak News (WHO DON), UNICEF Cholera platform, Reliefweb, PROMED and Global Incidence Map. A systematic review for evaluation reports was conducted using the MEDLINE, EMBASE, Global Health, Web of Science, WPRIM, Reliefweb, PDQ Evidence and CINAHL Plus databases, complemented by grey literature searches using Google and Google Scholar. Evaluation records were quality-scored and linked to epidemics based on time and place. The time period for the review was 2010-2019. RESULTS: A total of 429 epidemics were identified, primarily in sub-Saharan Africa, the Middle East and Central Asia. A total of 15,424 potential evaluations records were screened, 699 assessed for eligibility and 132 included for narrative synthesis. Only one tenth of epidemics had a corresponding response evaluation. Overall, there was wide variability in the quality, content as well as in the disease coverage of evaluation reports. CONCLUSION: The current state of evaluations of responses to these epidemics reveals large gaps in coverage and quality and bears important implications for health equity and accountability to affected populations. The limited availability of epidemic response evaluations prevents improvements to future public health response. The diversity of emphasis and methods of available evaluations limits comparison across responses and time. In order to improve future response and save lives, there is a pressing need to develop a standardized and practical approach as well as governance arrangements to ensure the systematic conduct of epidemic response evaluations in low-income and crisis settings.

Early detection of cholera epidemics to support control in fragile states: estimation of delays and potential epidemic sizes.

BACKGROUND: Cholera epidemics continue to challenge disease control, particularly in fragile and conflict-affected states. Rapid detection and response to small cholera clusters is key for efficient control before an epidemic propagates. To understand the capacity for early response in fragile states, we investigated delays in outbreak detection, investigation, response, and laboratory confirmation, and we estimated epidemic sizes. We assessed predictors of delays, and annual changes in response time. METHODS: We compiled a list of cholera outbreaks in fragile and conflict-affected states from 2008 to 2019. We searched for peer-reviewed articles and epidemiological reports. We evaluated delays from the dates of symptom onset of the primary case, and the earliest dates of outbreak detection, investigation, response, and confirmation. Information on how the outbreak was alerted was summarized. A branching process model was used to estimate epidemic size at each delay. Regression models were used to investigate the association between predictors and delays to response. RESULTS: Seventy-six outbreaks from 34 countries were included. Median delays spanned 1-2 weeks: from symptom onset of the primary case to presentation at the health facility (5 days, IQR 5-5), detection (5 days, IQR 5-6), investigation (7 days, IQR 5.8-13.3), response (10 days, IQR 7-18), and confirmation (11 days, IQR 7-16). In the model simulation, the median delay to response (10 days) with 3 seed cases led to a median epidemic size of 12 cases (upper range, 47) and 8% of outbreaks ≥ 20 cases (increasing to 32% with a 30-day delay to response). Increased outbreak size at detection (10 seed cases) and a 10-day median delay to response resulted in an epidemic size of 34 cases (upper range 67 cases) and < 1% of outbreaks < 20 cases. We estimated an annual global decrease in delay to response of 5.2% (95% CI 0.5-9.6, p = 0.03). Outbreaks signaled by immediate alerts were associated with a reduction in delay to response of 39.3% (95% CI 5.7-61.0, p = 0.03). CONCLUSIONS: From 2008 to 2019, median delays from symptom onset of the primary case to case presentation and to response were 5 days and 10 days, respectively. Our model simulations suggest that depending on the outbreak size (3 versus 10 seed cases), in 8 to 99% of scenarios, a 10-day delay to response would result in large clusters that would be difficult to contain. Improving the delay to response involves rethinking the integration at local levels of event-based detection, rapid diagnostic testing for cluster validation, and integrated alert, investigation, and response.

Response strategies for COVID-19 epidemics in African settings: a mathematical modelling study.

BACKGROUND: The health impact of COVID-19 may differ in African settings as compared to countries in Europe or China due to demographic, epidemiological, environmental and socio-economic factors. We evaluated strategies to reduce SARS-CoV-2 burden in African countries, so as to support decisions that balance minimising mortality, protecting health services and safeguarding livelihoods. METHODS: We used a Susceptible-Exposed-Infectious-Recovered mathematical model, stratified by age, to predict the evolution of COVID-19 epidemics in three countries representing a range of age distributions in Africa (from oldest to youngest average age: Mauritius, Nigeria and Niger), under various effectiveness assumptions for combinations of different non-pharmaceutical interventions: self-isolation of symptomatic people, physical distancing and 'shielding' (physical isolation) of the high-risk population. We adapted model parameters to better represent uncertainty about what might be expected in African populations, in particular by shifting the distribution of severity risk towards younger ages and increasing the case-fatality ratio. We also present sensitivity analyses for key model parameters subject to uncertainty. RESULTS: We predicted median symptomatic attack rates over the first 12 months of 23% (Niger) to 42% (Mauritius), peaking at 2-4 months, if epidemics were unmitigated. Self-isolation while symptomatic had a maximum impact of about 30% on reducing severe cases, while the impact of physical distancing varied widely depending on percent contact reduction and R0. The effect of shielding high-risk people, e.g. by rehousing them in physical isolation, was sensitive mainly to residual contact with low-risk people, and to a lesser extent to contact among shielded individuals. Mitigation strategies incorporating self-isolation of symptomatic individuals, moderate physical distancing and high uptake of shielding reduced predicted peak bed demand and mortality by around 50%. Lockdowns delayed epidemics by about 3 months. Estimates were sensitive to differences in age-specific social mixing patterns, as published in the literature, and assumptions on transmissibility, infectiousness of asymptomatic cases and risk of severe disease or death by age. CONCLUSIONS: In African settings, as elsewhere, current evidence suggests large COVID-19 epidemics are expected. However, African countries have fewer means to suppress transmission and manage cases. We found that self-isolation of symptomatic persons and general physical distancing are unlikely to avert very large epidemics, unless distancing takes the form of stringent lockdown measures. However, both interventions help to mitigate the epidemic. Shielding of high-risk individuals can reduce health service demand and, even more markedly, mortality if it features high uptake and low contact of shielded and unshielded people, with no increase in contact among shielded people. Strategies combining self-isolation, moderate physical distancing and shielding could achieve substantial reductions in mortality in African countries. Temporary lockdowns, where socioeconomically acceptable, can help gain crucial time for planning and expanding health service capacity.

Improving health information systems during an emergency: lessons and recommendations from an Ebola treatment centre in Sierra Leone.

BACKGROUND: The 2014-2016 West Africa Ebola epidemic highlighted the difficulty of collecting patient information during emergencies, especially in highly infectious environments. Health information systems (HISs) appropriate for such settings were lacking prior to this outbreak. Here we describe our development and implementation of paper and electronic HISs at the Sierra Leone Kerry Town Ebola treatment centre (ETC) from 2014 to 2015. We share our approach, experiences, and recommendations for future health emergencies. METHODS: We developed eight fact-finding questions about data-related needs, priorities, and restrictions at the ETC ("inputs") to inform eight structural decisions ("outputs") across six core HIS components. Semi-structured interviews about the "inputs" were then conducted with HIS stakeholders, chosen based on their teams' involvement in ETC HIS-related activities. Their responses were used to formulate the "output" results to guide the HIS design. We implemented the HIS using an Agile approach, monitored system usage, and developed a structured questionnaire on user experiences and opinions. RESULTS: Some key "input" responses were: 1) data needs for priorities (patient care, mandatory reporting); 2) challenges around infection control, limited equipment, and staff clinical/language proficiencies; 3) patient/clinical flows; and 4) weak points from staff turnover, infection control, and changing protocols. Key outputs included: 1) determining essential data, 2) data tool design decisions (e.g. large font sizes, checkboxes/buttons), 3) data communication methods (e.g. radio, "collective memory"), 4) error reduction methods (e.g. check digits, pre-written wristbands), and 5) data storage options (e.g. encrypted files, accessible folders). Implementation involved building data collection tools (e.g. 13 forms), preparing the systems (e.g. supplies), training staff, and maintenance (e.g. removing old forms). Most patients had basic (100%, n = 456/456), drug (96.9%, n = 442/456), and additional clinical/epidemiological (98.9%, n = 451/456) data stored. The questionnaire responses highlighted the importance of usability and simplicity in the HIS. CONCLUSIONS: HISs during emergencies are often ad-hoc and disjointed, but systematic design and implementation can lead to high-quality systems focused on efficiency and ease of use. Many of the processes used and lessons learned from our work are generalizable to other health emergencies. Improvements should be started now to have rapidly adaptable and deployable HISs ready for the next health emergency.

Variability in Intrahousehold Transmission of Ebola Virus, and Estimation of the Household Secondary Attack Rate.

Transmission between family members accounts for most Ebola virus transmission, but little is known about determinants of intrahousehold spread. From detailed exposure histories, intrahousehold transmission chains were created for 94 households of Ebola survivors in Sierra Leone: 109 (co-)primary cases gave rise to 317 subsequent cases (0-100% of those exposed). Larger households were more likely to have subsequent cases, and the proportion of household members affected depended on individual and household-level factors. More transmissions occurred from older than from younger cases, and from those with more severe disease. The estimated household secondary attack rate was 18%.

Development of a Pediatric Ebola Predictive Score, Sierra Leone1.

We compared children who were positive for Ebola virus disease (EVD) with those who were negative to derive a pediatric EVD predictor (PEP) score. We collected data on all children <13 years of age admitted to 11 Ebola holding units in Sierra Leone during August 2014-March 2015 and performed multivariable logistic regression. Among 1,054 children, 309 (29%) were EVD positive and 697 (66%) EVD negative, with 48 (5%) missing. Contact history, conjunctivitis, and age were the strongest positive predictors for EVD. The PEP score had an area under receiver operating characteristics curve of 0.80. A PEP score of 7/10 was 92% specific and 44% sensitive; 3/10 was 30% specific, 94% sensitive. The PEP score could correctly classify 79%-90% of children and could be used to facilitate triage into risk categories, depending on the sensitivity or specificity required.

Artemether-lumefantrine dosing for malaria treatment in young children and pregnant women: A pharmacokinetic-pharmacodynamic meta-analysis.

BACKGROUND: The fixed dose combination of artemether-lumefantrine (AL) is the most widely used treatment for uncomplicated Plasmodium falciparum malaria. Relatively lower cure rates and lumefantrine levels have been reported in young children and in pregnant women during their second and third trimester. The aim of this study was to investigate the pharmacokinetic and pharmacodynamic properties of lumefantrine and the pharmacokinetic properties of its metabolite, desbutyl-lumefantrine, in order to inform optimal dosing regimens in all patient populations. METHODS AND FINDINGS: A search in PubMed, Embase, ClinicalTrials.gov, Google Scholar, conference proceedings, and the WorldWide Antimalarial Resistance Network (WWARN) pharmacology database identified 31 relevant clinical studies published between 1 January 1990 and 31 December 2012, with 4,546 patients in whom lumefantrine concentrations were measured. Under the auspices of WWARN, relevant individual concentration-time data, clinical covariates, and outcome data from 4,122 patients were made available and pooled for the meta-analysis. The developed lumefantrine population pharmacokinetic model was used for dose optimisation through in silico simulations. Venous plasma lumefantrine concentrations 7 days after starting standard AL treatment were 24.2% and 13.4% lower in children weighing <15 kg and 15-25 kg, respectively, and 20.2% lower in pregnant women compared with non-pregnant adults. Lumefantrine exposure decreased with increasing pre-treatment parasitaemia, and the dose limitation on absorption of lumefantrine was substantial. Simulations using the lumefantrine pharmacokinetic model suggest that, in young children and pregnant women beyond the first trimester, lengthening the dose regimen (twice daily for 5 days) and, to a lesser extent, intensifying the frequency of dosing (3 times daily for 3 days) would be more efficacious than using higher individual doses in the current standard treatment regimen (twice daily for 3 days). The model was developed using venous plasma data from patients receiving intact tablets with fat, and evaluations of alternative dosing regimens were consequently only representative for venous plasma after administration of intact tablets with fat. The absence of artemether-dihydroartemisinin data limited the prediction of parasite killing rates and recrudescent infections. Thus, the suggested optimised dosing schedule was based on the pharmacokinetic endpoint of lumefantrine plasma exposure at day 7. CONCLUSIONS: Our findings suggest that revised AL dosing regimens for young children and pregnant women would improve drug exposure but would require longer or more complex schedules. These dosing regimens should be evaluated in prospective clinical studies to determine whether they would improve cure rates, demonstrate adequate safety, and thereby prolong the useful therapeutic life of this valuable antimalarial treatment.

Public health information in crisis-affected populations: a review of methods and their use for advocacy and action.

Valid and timely information about various domains of public health underpins the effectiveness of humanitarian public health interventions in crises. However, obstacles including insecurity, insufficient resources and skills for data collection and analysis, and absence of validated methods combine to hamper the quantity and quality of public health information available to humanitarian responders. This paper, the second in a Series of four papers, reviews available methods to collect public health data pertaining to different domains of health and health services in crisis settings, including population size and composition, exposure to armed attacks, sexual and gender-based violence, food security and feeding practices, nutritional status, physical and mental health outcomes, public health service availability, coverage and effectiveness, and mortality. The paper also quantifies the availability of a minimal essential set of information in large armed conflict and natural disaster crises since 2010: we show that information was available and timely only in a small minority of cases. On the basis of this observation, we propose an agenda for methodological research and steps required to improve on the current use of available methods. This proposition includes setting up a dedicated interagency service for public health information and epidemiology in crises.

Decision-making in humanitarian crises: politics, and not only evidence, is the problem.

Accurate, relevant and timely public health information is paramount in a humanitarian crisis: it can help to identify needs and priorities, guide decisions on interventions and resource allocation, monitor trends, evaluate the effectiveness of the response, support advocacy for human rights, and extract lessons that could be relevant in similar contexts. The present review shows, however, that the public health information available in humanitarian crises is, in general, inadequate and that its application is secondary to reasoning and incentives of a political nature, thus contributing to the recurrent failings of humanitarian action. This article reviews the causes of this state of affairs - cultural, political/institutional/methodological and ethical - that hinder the production, dissemination, and use of information for determining which interventions should be implemented or modified. Traditional epidemiological skills and methods are poorly suited to humanitarian contexts. The approaches and tools that have been introduced in crisis contexts require validation and improvement. There is a need for more field "barefoot epidemiologists" who are able to collaborate with anthropologists, demographers, and sociologists to better understand the priorities to be addressed in a crisis. Evidence, however, is not enough per se: it is political will that is the key factor in the use, or not, of information in decision-making concerning humanitarian resources and interventions.

Symptom- and Laboratory-Based Ebola Risk Scores to Differentiate Likely Ebola Infections.

Rapidly identifying likely Ebola patients is difficult because of a broad case definition, overlap of symptoms with common illnesses, and lack of rapid diagnostics. However, rapid identification is critical for care and containment of contagion. We analyzed retrospective data from 252 Ebola-positive and 172 Ebola-negative patients at a Sierra Leone Ebola treatment center to develop easy-to-use risk scores, based on symptoms and laboratory tests (if available), to stratify triaged patients by their likelihood of having Ebola infection. Headache, diarrhea, difficulty breathing, nausea/vomiting, loss of appetite, and conjunctivitis comprised the symptom-based score. The laboratory-based score also included creatinine, creatine kinase, alanine aminotransferase, and total bilirubin. This risk score correctly identified 92% of Ebola-positive patients as high risk for infection; both scores correctly classified >70% of Ebola-negative patients as low or medium risk. Clinicians can use these risk scores to gauge the likelihood of triaged patients having Ebola while awaiting laboratory confirmation.

Development and Deployment of the OpenMRS-Ebola Electronic Health Record System for an Ebola Treatment Center in Sierra Leone.

BACKGROUND: Stringent infection control requirements at Ebola treatment centers (ETCs), which are specialized facilities for isolating and treating Ebola patients, create substantial challenges for recording and reviewing patient information. During the 2014-2016 West African Ebola epidemic, paper-based data collection systems at ETCs compromised the quality, quantity, and confidentiality of patient data. Electronic health record (EHR) systems have the potential to address such problems, with benefits for patient care, surveillance, and research. However, no suitable software was available for deployment when large-scale ETCs opened as the epidemic escalated in 2014. OBJECTIVE: We present our work on rapidly developing and deploying OpenMRS-Ebola, an EHR system for the Kerry Town ETC in Sierra Leone. We describe our experience, lessons learned, and recommendations for future health emergencies. METHODS: We used the OpenMRS platform and Agile software development approaches to build OpenMRS-Ebola. Key features of our work included daily communications between the development team and ground-based operations team, iterative processes, and phased development and implementation. We made design decisions based on the restrictions of the ETC environment and regular user feedback. To evaluate the system, we conducted predeployment user questionnaires and compared the EHR records with duplicate paper records. RESULTS: We successfully built OpenMRS-Ebola, a modular stand-alone EHR system with a tablet-based application for infectious patient wards and a desktop-based application for noninfectious areas. OpenMRS-Ebola supports patient tracking (registration, bed allocation, and discharge); recording of vital signs and symptoms; medication and intravenous fluid ordering and monitoring; laboratory results; clinician notes; and data export. It displays relevant patient information to clinicians in infectious and noninfectious zones. We implemented phase 1 (patient tracking; drug ordering and monitoring) after 2.5 months of full-time development. OpenMRS-Ebola was used for 112 patient registrations, 569 prescription orders, and 971 medication administration recordings. We were unable to fully implement phases 2 and 3 as the ETC closed because of a decrease in new Ebola cases. The phase 1 evaluation suggested that OpenMRS-Ebola worked well in the context of the rollout, and the user feedback was positive. CONCLUSIONS: To our knowledge, OpenMRS-Ebola is the most comprehensive adaptable clinical EHR built for a low-resource setting health emergency. It is designed to address the main challenges of data collection in highly infectious environments that require robust infection prevention and control measures and it is interoperable with other electronic health systems. Although we built and deployed OpenMRS-Ebola more rapidly than typical software, our work highlights the challenges of having to develop an appropriate system during an emergency rather than being able to rapidly adapt an existing one. Lessons learned from this and previous emergencies should be used to ensure that a set of well-designed, easy-to-use, pretested health software is ready for quick deployment in future.

Exposure-Specific and Age-Specific Attack Rates for Ebola Virus Disease in Ebola-Affected Households, Sierra Leone.

Using histories of household members of Ebola virus disease (EVD) survivors in Sierra Leone, we calculated risk of EVD by age and exposure level, adjusting for confounding and clustering, and estimated relative risks. Of 937 household members in 94 households, 448 (48%) had had EVD. Highly correlated with exposure, EVD risk ranged from 83% for touching a corpse to 8% for minimal contact and varied by age group: 43% for children <2 years of age; 30% for those 5-14 years of age; and >60% for adults >30 years of age. Compared with risk for persons 20-29 years of age, exposure-adjusted relative risks were lower for those 5-9 (0.70), 10-14 (0.64), and 15-19 (0.71) years of age but not for children <2 (0.92) or 2-4 (0.97) years of age. Lower risk for 5-19-year-olds, after adjustment for exposure, suggests decreased susceptibility in this group.

Ebola Virus Disease in Children, Sierra Leone, 2014-2015.

Little is known about potentially modifiable factors in Ebola virus disease in children. We undertook a retrospective cohort study of children <13 years old admitted to 11 Ebola holding units in the Western Area, Sierra Leone, during 2014-2015 to identify factors affecting outcome. Primary outcome was death or discharge after transfer to Ebola treatment centers. All 309 Ebola virus-positive children 2 days-12 years old were included; outcomes were available for 282 (91%). Case-fatality was 57%, and 55% of deaths occurred in Ebola holding units. Blood test results showed hypoglycemia and hepatic/renal dysfunction. Death occurred swiftly (median 3 days after admission) and was associated with younger age and diarrhea. Despite triangulation of information from multiple sources, data availability was limited, and we identified no modifiable factors substantially affecting death. In future Ebola virus disease epidemics, robust, rapid data collection is vital to determine effectiveness of interventions for children.