School of Public Health
Phylodynamics of HIV and hepatitis C among PWID in India
People who inject drugs (PWID) bear a high burden of HIV and hepatitis C (HCV) infections worldwide. Understanding the complex and multi-dimensional risks for continued spread of HIV and HCV requires not only a focus on the individual, but also structural and network-level factors which drive transmission. Although we have an established understanding of the epidemiology of these diseases and how they are transmitted, we do not fully understand key aspects of transmission dynamics in real-world networks. A better understanding of these dynamics within networks of PWID will better inform public health programs as well as guide targeted interventions to more efficiently prevent disease and interrupt transmission in response to the opioid epidemic. Traditional questionnaire-based methods to evaluate this and to reconstruct viral transmission chains are subject to bias and cannot fully capture all the nuanced aspects of transmission dynamics. Our group has been working to integrate viral sequencing and phylogenetics with epidemiologic and network data collected from 12 cross-sectional studies of PWIDs in India to better understand HIV/HCV phylodynamics and the factors associated with disease transmission across differing stages of HIV and drug-use epidemics. A molecular epidemiology approach such as this facilitates the identification of key risk factors within the network that can be prioritized in HIV care clinics and public health programs while using genetics to overcome the biases of more traditional methods in infectious disease epidemiology.
PI Mentor: Shruti Mehta
People who inject drugs (PWID) bear a high burden of HIV and hepatitis C (HCV) infections worldwide, and injection drug use is increasingly accounting for a large portion HIV incidence in several low- and middle-income countries (LMICs). India has the third-largest number of people infected with HIV and the largest number of opioid users in the world. Understanding the complex and multi-dimensional risks for the continued spread of HIV and HCV requires not only a focus on the individual, but also structural and network-level factors which shape HIV and HCV transmission dynamics. Funding support from the Johns Hopkins Center for Global Health, enabled me to travel to India and gain first-hand experience with these populations in an LMIC setting. This experience complemented and built upon my area of expertise and provided cultural context that has helped me better understand the local Indian health systems and appropriately tailor my work to achieve its highest potential impact.
Despite the academic knowledge one may accrue about these populations and their corresponding public health needs, nothing truly compares to experiencing them first-hand in-country. During my time in India I visited several public and private hospitals, laboratories, research sites, and care clinics. I could go into great length describing the challenges, intricacies, and perceived inefficiencies of the health system, but perhaps one of the most jarring aspects for me was simply the sheer volume of patients. During a visit to one of the government hospitals in New Delhi, I squeezed through long winding queues of patients to meet staff and speak with the hospital’s two HIV counselors. I learned that both counselors regularly see over a hundred patients per day, many of them traveling great distances to receive care. Visits and meetings like this gave me valuable perspective on public health challenges in the Indian context, and I began thinking about how my work could be applied to improve efficiencies along the HIV care continuum to possibly elevate some of the burden currently weighing on the government health system. For example, HIV counselors in India provide pre- and post-test counseling for all patients regardless of their HIV test result. Some of my work in machine learning methods could be translated to classify patients into risk strata such that patients at high-risk of anti-retroviral treatment failure (or HIV infection in the case of negatives) could be prioritized for more detailed counseling, motivational interviewing, or peer navigators, while low-risk patients could receive shorter counseling or mobile delivery of negative results, creating a more efficient use of human resources. Additionally, much of my work in phylogenetics and molecular epidemiology could be used to identify networks in which HIV is spreading rapidly, improving surveillance and allowing for resources to be effectively directed to where they are needed most. These were not the initial aims of my research, but my experiences in India helped me better understand the local needs and helped me focus and extend my work to meet them. To me, this is the incredible power of cultural exchange, and I am deeply grateful for the support from the Johns Hopkins Center for Global Health that afforded me the field experience and contextual understanding that has truly helped my work reach its greatest potential.
The Government of India’s Ministry of Health and Family Welfare and National AIDS Control Organization later invited me to give talks on phylogenetics and machine learning at their National Consultation on Global and Local Evidence to Improve the HIV Cascade in India. Over the course of this meeting I worked with government officials from across India to chart a plan for improving steps along the care continuum that will reduce inefficiencies and help India reach its UNAIDS 90-90-90 targets. All in all, this experience taught me so much and I look forward to building long-lasting partnerships that have come out of it. Thank you to the Center for Global Health and all the donors that made this possible!