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Liana Woskie and Mosoka Fallah use the Ebola outbreak in Liberia to better understand the role and consequences of distrust in health systems and how it affects universal health coverage

Enlist community members to find and support cases — trust is crucial to containment. Enlist community members to find and support cases — trust is crucial to containment. Mosoka P. Fallah

The west African Ebola epidemic that began in 2013 exposed deep inadequacies in the national and international institutions responsible for protecting the public from the far-reaching human, social, economic, and political consequences of infectious disease outbreaks. The Ebola epidemic raised a crucial question: what reforms are needed to mend the fragile global system for outbreak prevention and response, rebuild confidence, and prevent future disasters?

Despite casting themselves as the protectors of universal human rights, many Euro-American leaders continued to enforce a global order in which the value of life was determined by geography and economic status. Behind visions of jet-setting “disease hunters”, travel bans, and rapid response teams of hazmat-clad scientists and military operatives, there is an assumption that security in HICs could be achieved through systems of surveillance, quarantine, and isolation that aim to confine biological threats posed by “foreign” bodies such that disease burns where it arises without ever reaching “our” shores. [...]these frameworks cannot be substitutes for the urgent task of building wider systems for care and disease prevention, including access to clean air and water, basic income, housing, and environmental regulations alongside medicines and health care. In light of this reality, HIC leaders––in coordination with their LIMC counterparts––must mobilise now to create strong public health systems for both immediate and long-term benefit.

Poverty has been implicated as a challenge in the control of the current Ebola outbreak in West Africa. Although disparities between affected countries have been appreciated, disparities within West African countries have not been investigated as drivers of Ebola transmission. To quantify the role that poverty plays in the transmission of Ebola, we analyzed heterogeneity of Ebola incidence and transmission factors among over 300 communities, categorized by socioeconomic status (SES), within Montserrado County, Liberia. We evaluated 4,437 Ebola cases reported between February 28, 2014 and December 1, 2014 for Montserrado County to determine SES-stratified temporal trends and drivers of Ebola transmission. A dataset including dates of symptom onset, hospitalization, and death, and specified community of residence was used to stratify cases into high, middle and low SES. Additionally, information about 9,129 contacts was provided for a subset of 1,585 traced individuals. To evaluate transmission within and across socioeconomic subpopulations, as well as over the trajectory of the outbreak, we analyzed these data with a time-dependent stochastic model. Cases in the most impoverished communities reported three more contacts on average than cases in high SES communities (p<0.001). Our transmission model shows that infected individuals from middle and low SES communities were associated with 1.5 (95% CI: 1.4-1.6) and 3.5 (95% CI: 3.1-3.9) times as many secondary cases as those from high SES communities, respectively. Furthermore, most of the spread of Ebola across Montserrado County originated from areas of lower SES. Individuals from areas of poverty were associated with high rates of transmission and spread of Ebola to other regions. Thus, Ebola could most effectively be prevented or contained if disease interventions were targeted to areas of extreme poverty and funding was dedicated to development projects that meet basic needs.

Ebola virus causes severe illness, often leading to death. Little is known about the health sequelae of those who survive infection. In this report, health outcomes over 12 months among nearly 1000 survivors of Ebola virus disease in Liberia are described.

While qualitative assessments of Ebola virus disease (EVD)-related stigma have been undertaken among survivors and the general public, quantitative tools and assessment targeting survivors have been lacking. Beginning in June 2015, EVD survivors from seven Liberian counties, where most of the country's EVD cases occurred, were eligible to enroll in a longitudinal cohort. Seven stigma questions were adapted from the People Living with HIV Stigma Index and asked to EVD survivors over the age of 12 at initial visit (median 358 days post-EVD) and 18 months later. Primary outcome was a 7-item EVD-related stigma index. Explanatory variables included age, gender, educational level, pregnancy status, post-EVD hospitalization, referred to medical care and EVD source. Proportional odds logistic regression models and generalized linear mixed-effects models were used to assess stigma at initial visit and over time. The stigma questions were administered to 859 EVD survivors at initial visit and 741 (86%) survivors at follow-up. While 63% of survivors reported any stigma at initial visit, only 5% reported any stigma at follow-up. Over the 18-month period, there was a significant decrease in stigma among EVD survivors (Adjusted Odds Ratio [AOR], 0.02; 95% Confidence Interval [CI], 0.01-0.04). At initial visit, having primary, junior high or vocational education, and being referred to medical care was associated with higher odds of stigma (educational level: AOR, 1.82; 95%CI, 1.27-2.62; referred: AOR, 1.50; 95%CI, 1.16-1.94). Compared to ages of 20-29, those who had ages of 12-19 or 50+ experienced lower odds of stigma (12-19: AOR, 0.32; 95%CI, 0.21-0.48; 50+: AOR, 0.58 95%CI, 0.37-0.91). Our data suggest that EVD-related stigma was much lower more than a year after active Ebola transmission ended in Liberia. Among survivors who screened negative for stigma, additional probing may be considered based on age, education, and referral to care.

The 2013–16 Ebola virus outbreak in west Africa was purported to have begun in the Guinean village of Meliandou in December, 2013.1 Authorities recorded 11 cases of Ebola virus disease (EVD) at this “index site” (where the virus is believed to have first spilled over into the human population), with 100% case fatality. The PREVAIL studies in Liberia are also revealing that, when longitudinally followed up, some individuals' titres switch from positive to negative and vice-versa, showing that identifying true Ebola virus infections by ELISA is not yet an exact science. Timothy and colleagues corroborate emerging evidence that suggest minimally symptomatic infections were common in the 2013–16 outbreak in west Africa and that a substantial portion of Ebola transmission events might have been undetected during the outbreak.4,5 Indeed, even WHO has admitted that the true toll of the epidemic “was certainly greater” than the 28 616 suspected, probable, and confirmed cases of EVD that were reported.6 Notwithstanding the minor limitation of recall bias, most will agree that this well designed study yields important insights into the genesis of the 2013–16 Ebola virus outbreak in west Africa; however, we must be wary of leaving epidemics to the epidemiologists.

[...]it is also possible the case left her village to evade cultural threats. [...]through community engagement, rumours need to be debunked and data generated. [...]practicing safe burials is essential.