Global Health Institute

Through the Global Health Initiative student fellowship program, I’ve had the opportunity this semester to work with Dr. Indira Narayanan on a project to improve the quality of neonatal care in Ghanaian hospitals and to assess the effectiveness of distance learning methods in quality improvement. The project involves a series of webinars, each focused on a different topic of concern in the neonatal units including birth asphyxia, hypothermia/temperature maintenance, and jaundice. 

Each session involves a case presentation by a Ghanaian hospital related to the topic of focus, which asks the presenting hospital to share examples of past cases. From this, a neonatal physician from the GUMC tailors a follow-on presentation to address the relevant issues faced in Ghanaian hospitals. Our team collaborates with each participating hospital to determine their primary area of concern, identify the shortcomings within the hospital that limit their effectiveness of treating the condition, and develop quality improvement activities accordingly. 

Following preliminary surveys it was determined a common issue in Ghanaian hospitals that impedes effective neonatal was lack of sufficient education of all personnel involved in care of the newborn. This was assessed to stem from infrequent training of hospital staff accompanied by high staff turnover rate. A potential accessible solution for this issue may be found through a distance learning program. 

Previously conducted distance learning programs in LMICs show successful models in course deployment and participation, as well as in resulting knowledge gains. In Zambia, a program organized by Johns Hopkins University in collaboration with Zambian experts on HIV received good feedback from participants, with significant increase in number of participants in each subsequent session. By the third session, more than 500 students had registered for the distance learning course. Another course focused on diagnosis, treatment, and prevention of tuberculosis involving the US, South Africa, India, and Pakistan resulted in an increase in the median test score from 66% to 86%, while a program in India on biostatistics and research ethics showed similar knowledge gains between on-site and online learning platforms immediately following course completion and in knowledge retention three months afterwards. 

These programs reflect good prospects for distance education in LMICs, however, more research is needed to examine the effectiveness of distance learning programs on clinical practice as well as in comparison to more standard methods of clinical training. Distance education also has the potential to contribute to more equitable healthcare in resource-poor settings by expanding access to training to community health workers who are the main providers of healthcare in more remote communities. If distance learning programs are found to be successful with effective clinical results, they can be a more accessible and flexible training method as they limit the need for travel, are more financially sustainable, and have lower opportunity costs for learners who must balance training with active patient care.

Sir Isaac Newton’s law of universal gravitation teaches us more than just how two masses attract each other from a distance. His famous equation relates the gravitational force between two objects to their respective masses and the distance separating the objects: 

F = G m1m2 

r2 

Interestingly, an essentially identical equation may be constructed that is capable of approximating migration flows between two locations, but this time as a function of population sizes (or even other measures) and distance separating the two locations. Generally, the equation below is referred to as a gravity model: 

Mij = G PiαPjβ 

rijγ 

where Mij represents the migrant population, Piα represents the population of the origin location (i), Pjβ represents the population of the destination location (j), and rijγ represents the distance between the two locations. G is just a proportionality constant for scaling purposes. α,β and γ are parameters usually estimated from data. 

My work this semester focused on estimating vaccine stockpile quantities for Nipah virus, a zoonotic virus (often transmitted by bats to humans) prevalent in India and Bangladesh. Estimating migration flow is of particular importance in modeling Nipah virus as it dictates how a disease may spread from location to location, via human travel. In turn, this will determine how and where vaccine stockpiles should be allotted. 

Often, data availability can be a barrier to research, since what data are available play a crucial role in what questions can be posed and answered. Thus, it is often necessary to create models that can closely approximate the unavailable data, based on a series of reasonable assumptions. In my case, because data on migration in India and Bangladesh are extremely scarce, a gravity model proved to be useful in providing estimates of population migration at the district level. I had data on district population and distances between districts, but needed to find values for the exponents in the model: α,β,γ. Briefly, the modeling process proceeded with reviewing travel and migration literature to understand how to properly parameterize the gravity model. After coding the model in Python, I tested various reported exponent values and plotted the results. I also attempted to validate my migration population data with summary-level data on migration in India. This served as a reality check to make sure the results were reasonable compared to what was expected. After some adjustments and scaling, the model output yielded promising results. 

In the larger scheme of the project, these migration estimates would feed into a larger model of Nipah virus transmission dynamics that would inform vaccine stockpile distribution. Vaccine stockpiling is an extremely important topic because it is one of our first lines of defense against the rampant spread of an epidemic or pandemic. It essentially puts the concept of prevention as the best form of a cure into practice. Additionally, estimates like these give policy-makers and vaccine manufacturers concrete, quantitative information on how to prepare for and prevent the spread of disease. 

Andrew Tiu (NHS `21) is an undergraduate studying human science and statistics. He is a student fellow with the Global Health Initiative.