Inequalities in levels of health between regions within a country are frequently regarded as a problem. Zimbabwe is characterised by poor and unequal conditions of health (both the state of people’s health and health services). The health system of the country shows severe spatial inequalities that are manifested at provincial, district and even local levels. This research therefore examines and analyses the spatial inequalities and temporal variation of health conditions in Zimbabwe. Composite indices were used to determine the people’s state of health in Zimbabwe. Administrative districts were ranked according to the level of people’s state of health. Cluster analysis was also performed to demarcate administrative districts according the level of health service provision. Districts with minimum difference were demarcated in a single cluster. Clusters were delineated using data on patterns of diseases and health and such clusters were used to demarcate the country’s spatial health system according to the Adapted Epidemiological Transition Model. This was used to evaluate the applicability of the model to Zimbabwe. It emerged from the research that generally the country’s health conditions are poor and the health system is characterised by severe spatial inequalities. Some districts are experiencing poor health service provision and serious health challenges and are still in the age of pestilence and famine but others have good health service provision as well as highly developed health conditions and are in the age degenerative diseases of the epidemiological transition model. It further emerged that the country’s health has been evolving with signs of improvement since the 1990s. Recommendations were made regarding possible adjustment to previous strategies and policies used in Zimbabwe, for the development of the health system of the country. New strategies were also recommended for the improvement of the health system of the country. Some proposals are made for further research on the spatial development of health in the country. / Geography / D. Litt et. Phil. (Geography)

In the post-World War II period, the population of poor and working-class Chinese New Yorkers––most of whom lived in Manhattan’s Chinatown––drastically increased in size and so too did the range of health problems they faced. This dissertation is the first in-depth historical study of Chinese Americans/New Yorkers’ postwar experiences with health education and activism. It documents the work of Chinese American grassroots activists and medical professionals to establish access to healthcare for Chinatown residents. By analyzing the transnational and cross-class dynamics of this movement, the dissertation challenges the long-standing assumption that the more well-to-do individuals of Chinese ancestry—especially recent professional immigrants from Taiwan and Hong Kong—had little interest in the wellbeing of their poorer counterparts. It also places Chinese New Yorker history alongside the better-known community control movements and health activism in Black and Latinx communities. The dissertation draws from research at 14 archives across the U.S., rare personal papers in Chinese and English, and interviews with over twenty Chinese American doctors and Chinatown activists. Before the mid-twentieth century, Chinese New Yorkers faced inequities in medical education and healthcare due to racially discriminatory policies and practices. From the 1940s to the 1960s, the end of Chinese exclusion and U.S. Cold War geopolitical interests in Asia allowed a select group of Chinese and Chinese American doctors and nurses to enter academic medicine and public health in the city. Chinese American public health nurses attracted public and private funding for much-needed social and health services in Chinatown by leveraging their transnational backgrounds and popular beliefs in the assimilation and integration of nonwhites. Meanwhile, the New York City-based American Bureau for Medical Aid to China and other American groups launched medical aid programs to help train medical personnel for the Nationalist Party and sustain its troops in their fight against the Chinese Communist Party. A subset of Chinese medical graduates from these programs drew from their hybrid credentials, contacts, and linguistic skills to obtain competitive jobs at hospitals and academic medical centers in New York and other American cities. Many of the transplanted Chinese medical graduates had intended to return to China after a short stint of advanced study in the U.S. But they decided to stay as a pragmatic response to political and social upheavals and constraints. Starting in the late 1960s, Chinatown’s rapidly expanding population, as well as the “maximum feasible participation” doctrine of President Lyndon Johnson’s War on Poverty initiative, made it strategic for the community watchdog agency, the Lower East Side Health Council-South, to court and include Chinatown residents in the fight for a better and new public hospital—Gouverneur Hospital. Inspired by the Civil Rights movement, workers’ struggles, and health radicalism in Black and Latinx communities, the Chinese American and Afro-Puerto Rican Health Council workers, Thomas Tam and Paul Ramos, implemented community programs and organized highly publicized and disruptive events, including a summer street health fair in Chinatown. Chinese New Yorkers of diverse migratory, class, age, and political backgrounds, including Chinese medical graduates who had the credentials and resources to serve their compatriots, played an integral role in these activities. In 1971, the cross-ethnic, cross-class coalition successfully demanded the opening and hiring of more bilingual personnel at the new Gouverneur. By the late 1970s, efforts led by Thomas Tam and Paul Ramos to bring the medical exam room into the Lower East Side became institutionalized in the form of the Chinatown and Betances Health Clinics. The clinics offered low-cost, comprehensive, and multilingual services, and encouraged professionals and youths of color to serve the community.

In a landmark report, the Secretary of Health and Human Services once characterized the legacy of disparities in healthcare and health outcomes in the United States as “an affront to our ideals and the ongoing genius of American medicine.” Since then, a vast amount of scientific evidence regarding health equity has been generated and important interventions developed. Yet despite substantial concerted efforts, health inequity remains a persistent and pervasive public health concern. A significant challenge is the lack of scalable resources to organize, synthesize, and integrate knowledge gleaned from available scientific evidence and real-world observational data in a comprehensive, systematic fashion. Recognizing this, the National Institute on Minority Health and Health Disparities recently embarked on a science visioning process and enumerated a set of strategic goals to foster a new generation of research capable of making major strides. Among the strategies proposed, the institute promotes development of new methods and measurements that enable 1) evaluation of health equity research and ensuring its relevance to a diversity of populations, 2) better leveraging and fostering linkage between existing and emerging data sources to enhance analytic capacity, and 3) analysis of health determinants contributing to health disparities among subpopulations and small groups. In alignment with this vision, the studies presented in this thesis seek to advance health equity science by developing, applying, and evaluating informatics-based approaches to support evidence synthesis, monitoring, and precision. In particular, systematic, scalable, and sustainable (i.e., reproducible) approaches are emphasized. This dissertation employs robust methods for big data collection, integration, and analysis while drawing from a rich set of existing and emerging data sources including a large corpus of biomedical literature, electronic health records from the largest public health information exchange in the United States, open datasets provided by public agencies, proprietary national insurance claims datasets, and public health reporting data. The dissertation first aims to facilitate large-scale evidence synthesis to identify major areas of focus in health equity research and elucidate potentially less well studied populations, conditions, and topics. In order to accomplish these tasks, it draws from the informatics toolbox by leveraging machine learning, natural language processing, and symbolic reasoning to assess a vast portfolio of research and compare it to real-world data on condition prevalence. In doing so, it spotlights potential paths for additional scientific inquiry and attention in public health practice. Ultimately, we find that there are indeed potential disparities in disparities research and elucidate less well studied areas of interest. Building on these insights, this thesis then leverages underutilized real-world data sources (e.g., health information exchanges) and a common data model to buttress the highly fragmented and outdated public health data infrastructure currently used to monitor conditions of interest and elucidate potential disparities among populations. By intention, the illustrative example presented operates at a scale commensurate with current regional public health reporting, increases the number and types of data elements available for analysis, and improves turnaround time for surveillance report generation. We found a substantial lack of alignment between testing practices and population- and neighborhood-level trends in cases of sexually transmitted infections, signaling potential disparities and inefficient resource allocation. Thus, our work meets several hallmarks of infectious disease surveillance in the era of big data including volume, variety, velocity, and value, respectively. Importantly, these findings were not otherwise or less likely detectable given existing public health reporting practices. Finally, as existing health equity literature and public health surveillance practices do not often incorporate intersectionality as an integral lens for drawing comparisons, in large part due to the technical intractability of examining all possible demographic intersections, we demonstrate a novel subgroup discovery approach tailored to elucidating intersectional disparities in health outcomes. In doing so, it aims to better inform efforts to recognize and engage subgroups who might benefit from greater attention and more closely tailored interventions. To our knowledge, this new approach is the first to leverage supervised clustering to operationalize intersectionality for health disparities research. To ground this work, clinical examples focus especially on demonstrating application of each new approach to generalizable use cases involving HIV and other sexually transmitted infections – highly stigmatized conditions for which there is a long history of inequity.