文章基本信息

标题：Addressing the Challenges of Research With Small Populations
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作者：Diane M. Korngiebel ; Maile Taualii ; Ralph Forquera 等
期刊名称：American journal of public health
印刷版ISSN：0090-0036
出版年度：2015
卷号：105
期号：9
页码：1744-1747
DOI：10.2105/AJPH.2015.302783
语种：English
出版社：American Public Health Association
摘要：Public health policy relies on accurate data, which are often unavailable for small populations, especially indigenous groups. Yet these groups have some of the worst health disparities in the United States, making it an ethical imperative to explore creative solutions to the problem of insufficient data. We discuss the limits of widely applied methods of data aggregation and propose a mixed-methods approach to data borrowing as a way to augment sample sizes. In this approach, community partners assist in selecting related populations that make suitable “neighbors” to enlarge the data pool. The result will be data that are substantial, accurate, and relevant to the needs of small populations, especially for health-related policy and decision-making at all levels. When President Obama signed US Executive Order 13515, he declared that no community should be invisible. 1 Yet for policymakers, the health status of small population groups, especially the indigenous peoples of the United States, remains largely hidden from view. Consistent epidemiological data are needed to inform policy decisions and resource allocation from the community level to the national level. For small population groups, such as American Indians, Alaska Natives, and Native Hawaiians, national reports and public data sets typically fail to provide sufficiently detailed information. Amassing enough accurate data requires innovative solutions, especially because small groups tend to have the largest health disparities. The scarcity of high-quality data means that these groups are often omitted from research agendas—or as the president put it, “Smaller communities in particular can get lost, their needs and concerns buried in a spreadsheet.” 1 As academics who conduct health research in small populations, we use community-based participatory methods within a theoretical framework that encompasses the social determinants of health. Our experience suggests some useful ways to address the problem of scarce data. One approach is to disaggregate data that lump together dissimilar populations, such as Native Hawaiians and Asian Americans, because aggregation can mask health disparities. Another approach is to augment data on extremely small populations by using statistical methods that borrow data from other groups with pertinent similarities to the population of interest. However, given the pitfalls inherent in data borrowing, we recommend qualitative methods that empower small communities to partner with academic researchers in selecting appropriate “neighbors,” whose adoption will maintain both the relevance and the distinctiveness of the resulting data pool. In the next sections, we describe a collaborative, multiperspective approach with broad application for small groups throughout the United States.