A mathematical model to estimate the incidence of child wasting in Yemen.

INTRODUCTION: The ongoing civil war in Yemen has severely restricted imports of food and fuel, disrupted livelihoods and displaced millions, worsening already high pre-war levels of food insecurity. Paired with frequent outbreaks of disease and a collapsed health system, this has brought rates of wasting in children under five to the country's highest recorded levels, which continue to increase as the crisis worsens and aid becomes increasingly limited. In their planning of services to treat and prevent wasting in children, humanitarian agencies rely on a standard calculation to estimate the expected number of cases for the coming year, where incidence is estimated from prevalence and the average duration of an episode of wasting. The average duration of an episode of moderate and severe wasting is currently estimated at 7.5 months-a globally-used value derived from historical cohort studies. Given that incidence varies considerably by context-where food production and availability, treatment coverage and disease rates all vary-a single estimate cannot be applied to all contexts, and especially not a highly unstable crisis setting such as Yemen. While recent studies have aimed to derive context-specific incidence estimates in several countries, little has been done to estimate the incidence of both moderate and severe wasting in Yemen. METHODS: In order to provide context-specific estimates of the average duration of an episode, and resultingly, incidence correction factors for moderate and severe wasting, we have developed a Markov model. Model inputs were estimated using a combination of treatment admission and outcome records compiled by the Yemen Nutrition Cluster, 2018 and 2019 SMART surveys, and other estimates from the literature. The model derived estimates for the governorate of Lahj, Yemen; it was initialized using August 2018 SMART survey prevalence data and run until October 2019-the date of the subsequent SMART survey. Using a process of repeated model calibration, the incidence correction factors for severe wasting and moderate wasting were found, validating the resulting prevalence against the recorded value from the 2019 SMART survey. RESULTS: The average durations of an episode of moderate and severe wasting were estimated at 4.86 months, for an incidence correction factor k of 2.59, and 3.86 months, for an incidence correction factor k of 3.11, respectively. It was found that the annual caseload of moderate wasting was 36% higher and the annual caseload of severe wasting 58% higher than the originally-assumed values, estimated with k = 1.6. CONCLUSION: The model-derived incidence rates, consistent with findings from other contexts that a global incidence correction factor cannot be sufficient, allow for improved, context-specific estimates of the burden of wasting in Yemen. In crisis settings such as Yemen where funding and resources are extremely limited, the model's outputs holistically capture the burden of wasting in a way that may guide effective decision-making and may help ensure that limited resources are allocated most effectively.

A predictive model for healthcare coverage in Yemen.

INTRODUCTION: The ongoing war in Yemen continues to pose challenges for healthcare coverage in the country especially with regards to critical gaps in information systems needed for planning and delivering health services. Restricted access to social services including safe drinking water and sanitation systems have likely led to an increase in the spread of diarrheal diseases which remains one of greatest sources of mortality in children under 5 years old. To overcome morbidity and mortality from diarrheal diseases among children in the context of severe information shortages, a predictive model is needed to determine the burden of diarrheal disease on Yemeni children and their ability to reach curative health services through an estimate of healthcare coverage. This will allow for national and local health authorities and humanitarian partners to make better informed decisions for planning and providing health care services. METHODS: A probabilistic Markov model was developed based on an analysis of Yemen's health facilities' clinical register data provided by UNICEF. The model combines this health system data with environmental and conflict-related factors such as the destruction of infrastructure (roads and health facilities) to fill in gaps in population-level data on the burden of diarrheal diseases on children under five, and the coverage rate of the under-five sick population with treatment services at primary care facilities. The model also provides estimates of the incidence rate, and treatment outcomes including treatment efficacy and mortality rate. RESULTS: By using alternatives to traditional healthcare data, the model was able to recreate the observed trends in treatment with no significant difference compared to provided validation data. Once validated, the model was used to predict the percent of sick children with diarrhea who were able to reach, and thus receive, treatment services (coverage rate) for 2019 which ranged between an average weekly minimum of 1.73% around the 28th week of the year to a weekly maximum coverage of just over 5% around the new year. These predictions can be translated into policy decisions such as when increased efforts are needed to reach children and what type of service delivery modalities may be the most effective. CONCLUSION: The model developed and presented in this manuscript shows a seasonal trend in the spread of diarrheal disease in children under five living in Yemen through a novel incorporation of weather, infrastructure and conflict parameters in the model. Our model also provides new information on the number of children seeking treatment and how this is influenced by the ongoing conflict. Despite the work of the national and local health authorities with the support of aid organizations, during the mid-year rains up to 98% of children with diarrhea are unable to receive treatment services. Thus, it is recommended that community outreach or other delivery modalities through which services are delivered in closer proximity to those in need should be scaled up prior to and during these periods. This would serve to increase number of children able to receive treatment by lessening the prohibitive travel burden, or access constraint, on families during these times.

Estimating access to health care in Yemen, a complex humanitarian emergency setting: a descriptive applied geospatial analysis.

BACKGROUND: In conflict settings, data to guide humanitarian and development responses are often scarce. Although geospatial analyses have been used to estimate health-care access in many countries, such techniques have not been widely applied to inform real-time operations in protracted health emergencies. Doing so could provide a more robust approach for identifying and prioritising populations in need, targeting assistance, and assessing impact. We aimed to use geospatial analyses to overcome such data gaps in Yemen, the site of one of the world's worst ongoing humanitarian crises. METHODS: We derived geospatial coordinates, functionality, and service availability data for Yemen health facilities from the Health Resources and Services Availability Monitoring System assessment done by WHO and the Yemen Ministry of Public Health and Population. We modelled population spatial distribution using high-resolution satellite imagery, UN population estimates, and census data. A road network grid was built from OpenStreetMap and satellite data and modified using UN Yemen Logistics Cluster data and other datasets to account for lines of conflict and road accessibility. Using this information, we created a geospatial network model to deduce the travel time of Yemeni people to their nearest health-care facilities. FINDINGS: In 2018, we estimated that nearly 8·8 million (30·6%) of the total estimated Yemeni population of 28·7 million people lived more than 30-min travel time from the nearest fully or partially functional public primary health-care facility, and more than 12·1 million (42·4%) Yemeni people lived more than 1 h from the nearest fully or partially functional public hospital, assuming access to motorised transport. We found that access varied widely by district and type of health service, with almost 40% of the population living more than 2 h from comprehensive emergency obstetric and surgical care. We identified and ranked districts according to the number of people living beyond acceptable travel times to facilities and services. We found substantial variability in access and that many front-line districts were among those with the poorest access. INTERPRETATION: These findings provide the most comprehensive estimates of geographical access to health care in Yemen since the outbreak of the current conflict, and they provide proof of concept for how geospatial techniques can be used to address data gaps and rigorously inform health programming. Such information is of crucial importance for humanitarian and development organisations seeking to improve effectiveness and accountability. FUNDING: Global Financing Facility for Women, Children, and Adolescents Trust Fund; Development and Data Science grant; and the Yemen Emergency Health and Nutrition Project, a partnership between the World Bank, UNICEF, and WHO.

Evidence of behaviour change during an Ebola virus disease outbreak, Sierra Leone.

OBJECTIVE: To evaluate changes in Ebola-related knowledge, attitudes and prevention practices during the Sierra Leone outbreak between 2014 and 2015. METHODS: Four cluster surveys were conducted: two before the outbreak peak (3499 participants) and two after (7104 participants). We assessed the effect of temporal and geographical factors on 16 knowledge, attitude and practice outcomes. FINDINGS: Fourteen of 16 knowledge, attitude and prevention practice outcomes improved across all regions from before to after the outbreak peak. The proportion of respondents willing to: (i) welcome Ebola survivors back into the community increased from 60.0% to 89.4% (adjusted odds ratio, aOR: 6.0; 95% confidence interval, CI: 3.9-9.1); and (ii) wait for a burial team following a relative's death increased from 86.0% to 95.9% (aOR: 4.4; 95% CI: 3.2-6.0). The proportion avoiding unsafe traditional burials increased from 27.3% to 48.2% (aOR: 3.1; 95% CI: 2.4-4.2) and the proportion believing spiritual healers can treat Ebola decreased from 15.9% to 5.0% (aOR: 0.2; 95% CI: 0.1-0.3). The likelihood respondents would wait for burial teams increased more in high-transmission (aOR: 6.2; 95% CI: 4.2-9.1) than low-transmission (aOR: 2.3; 95% CI: 1.4-3.8) regions. Self-reported avoidance of physical contact with corpses increased in high but not low-transmission regions, aOR: 1.9 (95% CI: 1.4-2.5) and aOR: 0.8 (95% CI: 0.6-1.2), respectively. CONCLUSION: Ebola knowledge, attitudes and prevention practices improved during the Sierra Leone outbreak, especially in high-transmission regions. Behaviourally-targeted community engagement should be prioritized early during outbreaks.

National survey of Ebola-related knowledge, attitudes and practices before the outbreak peak in Sierra Leone: August 2014.

BACKGROUND: The 2014-2015 Ebola epidemic in West Africa was the largest ever to occur. In the early phases, little was known about public knowledge, attitudes and practices (KAP) relating to Ebola virus disease (Ebola). Data were needed to develop evidence-driven strategies to address gaps in knowledge and practice. METHODS: In August 2014, we conducted interviews with 1413 randomly selected respondents from 9 out of 14 districts in Sierra Leone using multistage cluster sampling. Where suitable, Ebola-related KAP questions were adapted from other internationally validated questionnaires related to infectious diseases. RESULTS: All respondents were aware of Ebola. When asked unprompted, 60% of respondents could correctly cite fever, diarrhoea and vomiting as signs/symptoms of Ebola. A majority of respondents knew that avoiding infected blood and bodily fluids (87%) and contact with an infected corpse (85%) could prevent Ebola. However, there were also widespread misconceptions such as the belief that Ebola can be prevented by washing with salt and hot water (41%). Almost everyone interviewed (95%) expressed at least one discriminatory attitude towards Ebola survivors. Unprompted, self-reported actions taken to avoid Ebola infection included handwashing with soap (66%) and avoiding physical contact with patients with suspected Ebola (40%). CONCLUSION: Three months into the 2014 Ebola outbreak in Sierra Leone, our findings suggest there was high awareness of the disease but misconceptions and discriminatory attitudes toward survivors remained common. These findings directly informed the development of a national social mobilisation strategy and demonstrated the importance of KAP assessment early in an epidemic.