Finding the Needle in a Haystack: Why Small Sample Studies Matter to the Health Justice of Indigenous Communities in Taiwan?

Written by Kalesekes Kaciljaan (Yu-Chi, Huang) and Ta-Chun Hua.

Image credit: SAM_4150 by coolloud/ Flickr, license: CC BY-NC-ND 2.0. 

We often see that Indigenous-focused research is excluded from major research programs with the capacity to influence policymaking, ignoring the impact of Indigenous cultural distinctiveness on health disparities. For example, in the annual statistics of health promotion in Taiwan, the authors didn’t separate most disease-related statistics, such as prevalence, incidence, and age distribution of individual diseases, of Indigenous Peoples from the general population. 

It has been tough to present the extent of the health differences between Indigenous People and the rest of the Taiwanese population in the absence of these essential figures. Lack of information also posed many difficulties for community health practitioners attempting to establish a health promotion plan for undersized Indigenous communities. This phenomenon occurs in Taiwan and many other countries with multiple ethnic minorities. 

This article addresses how determining sample size in health-related research can contribute to health injustice and disparities among a small population of an ethnically or culturally distinct group, such as Indigenous communities in Taiwan or small communities worldwide.

Why do mainstream research societies often overlook the need to conduct small sample-size studies? The first of these reasons might be due to the inherent constraints of small sample-size investigations. As statistical principles implied, smaller sample-size studies were often seen as inferior to larger ones in terms of reliability and validity. Large sample-size studies are more likely to have the statistical power to support researchers’ claims about the tested hypothesis in their health studies.

But the large sample-size studies had their own drawbacks. To ensure the sample size for “creditable” reporting, researchers often mixed up several smaller populations. Large-scale health surveys in the U.S., for instance, usually labelled people from all Oceanic countries or territories under the category of NHPI, short for “Native Hawaiians and Pacific Islanders.” This act, however, disregarded the fact that Oceania is a vast area with diverse cultures, economies, politics, and colonial history, resulting in a wide range of health conditions. If these reports were used to represent the overall health status of every Oceanic community in the U.S, we might have the risk of racial misclassification. Some health conditions affecting minor populations might become “invisible” due to this process. 

Similar conditions also happen in Taiwan. In the annual report mentioned above, all of Taiwan’s Indigenous Peoples were labelled as one homogenous group. However, more than dozens of geographically, culturally, and historically distinct tribes of Indigenous Peoples are living in Taiwan. Moreover, few investigations were done to analyse the health statistics of different Indigenous Peoples by cultural, urban-rural, or economic aspects. 

Since differences are neutralised throughout the statistical process, health interventions based on these data may neglect the needs of some vulnerable groups and populations. Therefore, pursuing data volume is not necessarily essential in every scenario; instead, we should choose suitable statistical techniques based on sample sizes. If no appropriate method for the target population could be found, rigorous data aggregation based on specific demographic or behavioural variables, such as age, profession, education level, or income should be considered.

Other reasons might be attributed to the long-lasting assumptions about ethnic health issues within research communities. Because of the “neglectable” proportion to the entire population, Indigenous Peoples’ data on most health conditions were seldom examined individually. Most researchers anticipated that the impact would be insignificant, like a drop in the ocean. Some health conditions were singled out and analysed, but their selection might have been influenced by biased or even stigmatised perceptions of Indigenous Peoples’ health status quo. For example, the annual statistics mentioned above specifically compared Indigenous Peoples’ drinking and smoking statistics to the general population, but not their other health issues, nutrition and exercise status, or use of preventative services. 

An apparently more in-depth examination of individual disease groups was published in a separate dataset created in the Annual Report on Indigenous Peoples’ Population and Health by the Indigenous Peoples’ Council of Taiwan. However, looking deeper into the data, very diverse diseases could be categorised under the same disease group, thus hindering any focus on specific diseases. 

Some conditions, including alcoholic hepatitis, cirrhosis, and alcohol-related psychological disorders, were somehow identified, and studied individually as “focused diseases of the Indigenous Peoples.” Because of the long-standing stigma, these examples reflected the reality of how alcohol and tobacco use were overemphasised when discussing indigenous health in Taiwan. Other critical issues, such as metabolic diseases, diet imbalance and cancers, received less attention but claimed an increasing proportion of Indigenous People’s health and lives.

The need for meaningful health statistics representative of Indigenous Peoples’ current situation is urgent. More data on Indigenous Peoples’ health issues implies that health workers and policymakers will better grasp the extent of what they are facing. Focused studies of individual communities can also include their cultural knowledge systems and storytelling into health intervention plans. Possessing the ability to analyse and interpret health statistics for our own people represents one step closer to health sovereignty for Indigenous communities. It is beneficial to collect data and share data for evaluation and to identify health priorities for helping Indigenous populations while serving public health goals.

Obstacles to small sample-sized studies must still be overcome, but this can be accomplished by choosing appropriate statistical methods or making necessary adjustments. Working with statistics on Indigenous health issues is like finding the needle in a haystack of general data: the effort might appear exhausting or worthless, yet once grasped, the needle would pierce the impenetrable barrier to benefit Indigenous health.

Kalesekes Kaciljaan (Yu-Chi, Huang) is a third-year PhD student at the Office of Public Health Studies, Thompson School of Social Work & Public Health, the University of Hawaiʻi at Mānoa. She is from the Sapulju community of the Indigenous Paiwan People of Taiwan.

Ta-Chun Hua is a Resident physician at the Department of Family Medicine, Mackay Memorial Hospital, Taiwan.

This article is published as part of a special issue on Health Justice in Diversity

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s