Measles

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How International Travel and Vaccine Resistance have led to a resurgence of Measles in the U.S.

By Lauren Gardner
US Measles Risk Dashboard

This resurgence of measles cases is due to two main risk factors. The first, as previously noted by Olive et. al. and reiterated by Hotez, is a fall in vaccination rates fueled by anti-vaccination movements in both the U.S. and Europe. In the U.S. in particular there has been an upsurge of non-medical exemptions (NMEs) in the 18 states which allow it. Compounding the decrease in vaccination rates is the increase in international travel into these localities (where measles is supposed to have been eliminated through vaccination) from regions where measles remains endemic, and outbreaks are ongoing. The fact that measles is a highly contagious virus capable of airborne spread further increases the seriousness of this threat.

Olive et al. previously presented a “heat map” of measles risk based on NMEs for vaccination in U.S. counties. In collaboration with Sahotra Sarkar at UT Austin and others (Aleksa Zlojutro at UNSW Sydney and Kamran Khan at University of Toronto), we further investigated how this “heat map” of measles risk in the US might shift geographically when county level NME rates (that reveal pockets of low vaccination) were compounded with incoming international air travel volume weighted by the size of measles outbreaks abroad.

To do so, we performed an analysis that compounds four factors: 1) international air travel volume arriving from measles affected countries into each U.S. county, 2) county NME rates, 3) county population, and 4) the size (incidence rate) of the measles outbreak at travel origin country.  The analysis was conducted for each year between 2011 and 2019. The detailed methods and results of the analysis are soon to be published in Lancet Infectious Diseases. An interactive map illustrating the results of our analysis and corresponding data is available here. Additionally, the figures below illustrate the U.S. measles risk for 2019 computed by the model, and the corresponding global level measles outbreak data from WHO. (NOTE: the 2019 analysis uses WHO reported cases from each country up to April 19.)

Results from the 2019 analysis reveal the U.S. counties at highest risk of a measles outbreak at present, and are spatially consistent with the U.S. measles cases reported to date (April 19): either the counties we identify or those immediately adjacent to them are the ones that have reported measles cases.

Critically, the results correctly predict the areas in Washington, Oregon, and New York that have seen major measles outbreaks. The risk analysis also reveals U.S. localities that have not experienced a measles outbreak in 2019 but are at risk of imported measles cases resulting in a local outbreak because they lie adjacent to a county that has and/or is served by a major international airport. These include Travis (TX), Maricopa (AZ), Clayton (GA), Honolulu County (HI), Wayne (MI), Salt Lake (UT), Hennepin (MN), Suffolk (MA), Loudon (VA), San Diego (CA) and multiple counties in Florida. Additional surveillance should also target Cook (IL) and Los Angeles (CA), which have seen only one case so far, but due to the presence of major international airports, they may serve as the fulcrum of continuous importation of measles into the United States.

Lastly, this analysis also reveals the set of countries that contribute most to measles risk across the U.S. in 2019. The top countries ordered by risk posed are: Ukraine, Mexico, Cuba, Israel, Japan, Thailand and Philippines. Therefore, we recommend that surveillance should also be directed towards those U.S. counties with the high incoming passenger volume from these countries, which should themselves be targeted for vaccination efforts. Prof. Sarkar suggests additional actions for prevention in his blog.

For further information please see our publication in Lancet Infectious Diseases (Published Online May 9, 2019).

Figure 1: Top 25 U.S. Counties Predicted to be at Highest Risk for Measles in 2019.

This results in this figure correspond to our publication in Lancet Infectious Diseases (Published Online May 9, 2019).  Risk is measured by the expected relative size of a measles outbreak in a county.

Figure 2: Annual Measles Cases by Country as reported by WHO
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A set of female individuals with the above criteria were considered. Further demographic and diet considerations (in order to select similar patients) led to selecting 11 different individuals’ one day of intake as the initial dataset for the model. In another setting, we only considered people that have consumed a reasonable amount of sodium and water. We consider these two nutrients as the main constraints in the DASH diet. 



In order to compare different potential data and their performance with the model, we used different data groups from the NHANES database. A group of middle-aged women with certain similar characteristics and a group of people with certain attributes in their diets. In the first group, we did not consider how the individual’s daily diet is reflecting on the constraints that the forward problem had and we relied on their own personal answer to questions regarding hypertension and also how prone they thought they were to type-2 diabetes. The result was a sparse set of variables and an inconclusive optimal solution in regards to the preferences. In the second group, we tried to obtain sub-optimal data. We prioritized the maximum sodium intake constraint and the water intake constraints as our main and most important constraints. 

Many outpatient facilities with expensive resources, such as infusion and imaging centers, experience surge in their patient arrival at times and are under-utilization at other times. This pattern results in patient safety concerns, patient and staff dissatisfaction, and limitation in growth, among others. Scheduling practices is found to be one of the main contributors to this problem.

We developed a real-time scheduling framework to address the problem, specifically for infusion clinics. The algorithm assumes no knowledge of future appointments and does not change past appointments. Operational constraints are taken into account, and the algorithm can offer multiple choices to patients.

We generalize this framework to a new scheduling model and analyze its performance through competitive ratio. The resource utilization of the real-time algorithm is compared with an optimal algorithm, which knows the entire future. It can be proved that the competitive ratio of the scheduling algorithm is between 3/2 and 5/3 of an optimal algorithm.

This work was performed with the MIT/MGH Collaboration.

In many healthcare services, care is provided continuously, however, the care providers, e.g., doctors and nurses, work in shifts that are discrete. Hence, hand-offs between care providers is inevitable. Hand-offs are generally thought to effect patient care, although it is often hard to quantify the effects due to reverse causal effects between patients’ duration of stay and the number of hand-off events. We use a natural randomized control experiment, induced by physicians’ schedules, in teaching general medicine teams. We employ statistical tools to show that between the two randomly assigned groups of patients, a subset who experiences hand-off experience a different length of stay compared to the other group.

This work was performed with the MIT/MGH Collaboration.

Primary care is an important piece in the healthcare system that affects the downstream medical care of patients heavily. There are specific challenges in primary care as healthcare shifts from fee-for-service to population health management and medical home, focuses on cost savings and integrates quality measures. We consider the primary care unit at a large academic center that is facing similar challenges. In this work we focus on the imbalance in workload, which is a growing regulatory burden and directly concerns any staff in primary care. It can result in missed opportunities to deliver better patient care or providing a good work-environment for the physicians and the staff. We consider the primary care unit at the large academic center and focus on their challenge in balancing staff time with quality of care through a redesign of their system. We employ optimization models to reschedule providers’ sessions to improve the patient flow, and through that, a more balanced work-level for the support staff. 

This work was performed with the MIT/MGH Collaboration.

Perioperative services are one of the vital components of hospitals and any disruption in their operations can leave a downstream effect in the rest of the hospital. A large body of evidence links inefficiencies in perioperative throughput with adverse clinical outcomes. A regular delay in the operating room (OR), may lead to overcrowding in post-surgical units, and consequently, more overnight patients in the hospital. Conversely, an underutilization of OR is not only a waste of an expensive and high-demand resource, but it also means that other services who have a demand are not able to utilize OR. This mismatch in demand and utilization may, in turn, lead to hold-ups in the OR and cause further downstream utilization. We investigate the utilization of operating rooms by each service. The null hypothesis of this work is that the predicted utilization of the OR, i.e., the current block schedule, matches completely with the actual utilization of the service. We test this hypothesis for different utilization definitions, including physical and operational utilization and reject the null hypothesis. We further analyze why a mismatch may exist and how to optimize the schedule to improve patient flow in the hospital.