Risks of Death and Severe Disease in Patients With Middle East Respiratory Syndrome Coronavirus, 2012–2015

Caitlin M. Rivers; Maimuna S. Majumder; Eric T. Lofgren


Am J Epidemiol. 2016;184(6):460-464. 

In This Article


The emergence of a novel infectious disease presents a particular challenge to timely epidemiologic research, as the existence of sparse and irregularly collected data competes with the need to identify risk factors associated with the disease and its outcomes. A dearth of openly shared data impedes research efforts, such as the construction of mathematical models or broader-scale risk assessments. We have attempted to address this for MERS-CoV, using a regularly updated, publicly available data set. The use of multivariate models with allowance for extensively missing data has allowed the identification of some previously suggested risk factors that do not appear to be so upon adjustment for other covariates. For example, female patients were not necessarily at lower risk for disease after adjustment, nor were primary cases at higher risk for fatal infections. Issues of data quality and "missingness" during outbreaks necessitate the use of robust techniques for handling missing data.

We found that older age and underlying comorbidity were associated with increased risks of both death and severe disease. While not a surprising finding, this does suggest that older and sicker patients merit heightened vigilance. Additionally, cases arising progressively later during the epidemic have been associated with lower risks of both death and severe disease at the time of initial reporting, suggesting that treatment methods for MERS-CoV may be increasing in efficacy. Alternately, the proportion of mild and asymptomatic cases has been rising over time, suggesting that less severe cases are becoming more likely to be ascertained as a result of epidemiologic investigation. This is supported by temporal trends in the missingness of the data, which grows less severe later in the epidemic.

This study was not without limitations, especially those stemming from the data used. Patient outcomes were identified at the time of reporting, rather than based on follow-up, so it is possible that some patients counted as living or without severe disease may have experienced serious or fatal complications after reporting, which would not have been recorded in the data. There is also the possibility of unmeasured confounding biasing these estimates or the multiple imputation model not fully addressing the missingness within the data set. These issues are unlikely to be resolved without more resource-intensive population-based studies.

Despite these shortcomings, the study represents an attempt to quantify the known risk factors for MERS-CoV using the best available and open data. While the estimates are imperfect, they are superior to univariate associations that do not control for confounding, or allowing paralysis in the face of difficult and imperfect data to deprive public health planners of potentially useful information. These estimates can and should be revised as more becomes known about the disease, but for the moment, they represent the current state of our knowledge about MERS-CoV and its impact on human health outcomes.