Eric Ohemeng, a Ghanaian scholar making waves in the United States, is pioneering innovative approaches to enhance the accuracy and inclusivity of national health data systems. His work revolves around integrating machine learning with modern survey design principles, aiming to address the persistent underrepresentation of marginalized communities in these crucial datasets. He recognizes that skewed data resulting from the exclusion or misrepresentation of vulnerable populations like racial and ethnic minorities, low-income households, and rural communities leads to inefficient resource allocation and inequitable health policies. Ohemeng’s research focuses on developing predictive risk models and adaptive data collection techniques to rectify these systemic biases, ultimately striving to create health data systems that genuinely reflect the diversity of the population and ensure that the needs of all communities are met.
Ohemeng’s journey began with a master’s degree in Survey and Data Science from the University of Maryland’s prestigious Joint Program in Survey Methodology, a program deeply connected with leading statistical agencies like the U.S. Census Bureau and Westat. This rigorous training provided him with the advanced statistical and computational tools necessary to tackle complex challenges in data collection and analysis. Coupled with his experience supporting initiatives at the United Nations Statistics Division, where he worked on improving household surveys globally, Ohemeng developed a deep understanding of the intricacies of survey design, sampling frameworks, and data integration strategies. This experience reinforced his conviction that methodological rigor is essential for achieving equity in data representation, emphasizing that inaccurate or incomplete data not only undermines research but also hinders effective government responses, leaving vulnerable populations disproportionately affected.
A core component of Ohemeng’s work involves the development of adaptive survey designs, a cutting-edge approach that utilizes algorithms to dynamically adjust question flows and contact strategies during the survey process. This technique proves particularly valuable in engaging hard-to-reach populations, such as individuals with limited English proficiency, those lacking internet access, or those who harbor distrust towards institutions. Adaptive design allows for a more personalized and responsive approach to data collection, tailoring the survey experience to the specific needs and circumstances of each respondent, ultimately maximizing participation rates and improving data quality. This innovative approach aligns with the growing need for modernized data infrastructure within public health agencies, particularly in the wake of the COVID-19 pandemic, which highlighted the critical gaps in real-time data collection and community-level insights.
Ohemeng’s expertise is directly contributing to the transformation of public health data systems towards greater responsiveness, resilience, and equity. His focus on adaptive designs offers a cost-effective solution to improve data quality without requiring massive increases in resources. By tailoring the survey process to individual respondents, adaptive surveys can overcome barriers to participation and ensure that the voices of all communities are heard. This personalized approach not only improves the accuracy and representativeness of the data but also fosters trust and engagement with the survey process itself. Ohemeng’s contributions are helping to reshape how public health data is collected, analyzed, and utilized to inform policy decisions, ensuring that interventions are truly evidence-based and equitable in their impact.
While his current focus lies on enhancing U.S. data systems, Ohemeng’s vision extends far beyond national borders. He champions the belief that all countries, particularly those in the Global South, deserve robust, reliable, and inclusive data foundations to support economic development and health equity. He envisions a future where every nation has access to high-quality data that accurately reflects the diversity of its population and empowers evidence-based policymaking. This global perspective underscores the importance of collaboration and knowledge sharing across borders to strengthen data systems worldwide. Ohemeng’s work exemplifies a growing movement towards inclusive and ethical data innovation, promoting the development of data systems that are representative, trustworthy, and ultimately contribute to better health outcomes for all.
Ohemeng’s future aspirations involve continuing his work at the intersection of public health, survey statistics, and data science, collaborating with countries to develop policies grounded in evidence and driven by equity. Whether advising on sampling methodologies in Ghana, refining nonresponse models in the United States, or developing tools for pandemic preparedness on a global scale, his work signifies a paradigm shift towards more inclusive and ethical data practices. As global organizations and national governments grapple with multifaceted challenges, from infectious disease outbreaks to climate change, Eric Ohemeng embodies a new generation of African scholars transforming how the world collects, interprets, and acts upon public health data, striving for a future where data serves as a powerful tool for positive change and equitable progress.
