To our knowledge, this is the first study to investigate the prevalence of low HL and associated factors in the Wa ethnic group. our findings are as follows: (1) the prevalence of low HL among the Wa was by far the highest among Chinese ethnic minorities; and (2) demographic variables, including gender, age, education level, current smoking, and residence were associated with low HL.
Up to date, there has been only one survey of HL among the Dulong, a “direct fast-forward” ethnic group in China, with a rate 1.46% adequate HL [18]. Compared to this previous report, our result showed that the Wa had the lowest rate of adequate HL (0.89%). Pu et al. reported that the adequate level of health knowledge, health-related behaviors and lifestyles, and health-related skills among the Dolong were 2.11%, 3.14%, and 0.16%, respectively, while the Wa had adequate HL rates of 6.73%, 2.1% and 1.34%, respectively [18]. These results suggest that there was a significant lack of health-related skills among these two “direct fast-forward” ethnic groups, which may be a key point for health promotion interventions.
There are two main reasons that may contribute to the low level of HL among the “direct fast-forward” ethnic groups. First, the Wa and Dulong lived in mountainous areas until 1950 and maintained a traditional lifestyle of slash-and-burn farming. Due to natural, environmental, social, and historical issues [32,33], ethnic minorities tend to have low levels of HL and health status. Numerous studies have shown that high socioeconomic status [33], good education, and good living and working environments [34] weaken their exposure to risk factors affecting health [35] and increase the likelihood of acquiring HL [35]. Second, low HL is associated with limited knowledge of screening, lack of desire to screen, and language skills [36,37]. In this study, most villagers, especially the elderly, had low Chinese language proficiency, and their responses to the questionnaire required translation. However, there were no words or phrases in the Wa language to name disease terms such as cancer, hypertension, hepatitis B, and tuberculosis. Some information was more likely to be lost in translation during the survey. Specifically, of the eight villages, Nanlang village was the most backward, being far from the government office and health center, with no road access to the village until 2016. Villagers using bilingual or the Wa language at home with limited Chinese proficiency. Respondents in Nanlang village had significantly lower HL than those in other areas.
Our results are consistent with existing studies on ethnic minorities. For example, in the United States, 44.9% of low language proficient ethnic groups reported limited English proficiency, while only 13.8% of fluent English speakers had low HL [38]. More than half of Chinese Americans with limited English proficiency had low HL and cancer screening rates due to health communication barriers [32, 33]. These findings suggest that the inclusion of language proficiency in multivariate study factors associated with HL is critical to understanding all screening factors for low HL across ethnic groups, as limited language proficiency may compound vulnerabilities such as educational attainment, underutilization or misuse of health care resources, and decreased HL [34]. Therefore, language proficiency associated with HL should be quantified to better understand the predictors of low HL, to inform future interventions, and to remove appropriate barriers to HL [35]. There is growing evidence that multifaceted, culturally and linguistically appropriate interventions enable respondents with lower levels of health literacy to be more responsive [43,44] such as the use of colloquial language in screenings and interviews.
To further explore the risk factors for low HL in Wa, our results showed that demographic information including age, gender, education level, and village location were significantly associated with low HL through linear regression models. Previous studies have shown that the most common demographic characteristics related to HL were education, age [32], gender [45], ethnicity [46], residence [47], and income [48,49]. The meta-analysis study reported that of these variables, age, education, and race were the most consistently included in the regression equation [50]. Nearly all of our respondents had a low level of education, with 87.7% having an elementary or middle school level. Illiteracy accounted for 13.8% of the population, and the mean age of illiterates was 52 ± 10.44 years, of which 62% were female. This is due to limited educational opportunities for Chinese seniors at a young age, resulting in low education levels and even illiteracy [51]. Women typically have less access to higher education than men [52]. An individual’s low educational background often leads to negative attitudes toward health management [53,54], affecting HL and impacting access to health care services.
Besides, several barriers, such as geography, distance [55], bad weather [30], and rural or urban [15], contribute to low levels of HL. Rural-urban differences in health information sources may be due to structural barriers, such as a shortage of rural specialists, inadequate online resources [54], and greater distance from the nearest health facility [56]. Patients in rural areas are two to three times further away from medical appointments than those in urban areas, resulting in residents potentially having fewer opportunities to ask for or receive health information from specialists [57]. Similarly, among our participants, those who were further from the township (mean distance of 32 m) had lower HL than those closer to the township office (mean distance of 18 m). These results suggest that improving access to health information within the community is key to improving HL.
Few studies highlighted the relationship between BMI or WC and HL. Some studies have found a strong correlation between low HL and obesity [58], whereas others have shown that HL didn’t predict BMI [59]. Similarly, our study showed that the association between BMI or WC and HL was statistically significant but not a risk predictor. 37% and 48.1% of our respondents who were centrally obese and smokers, respectively, had significantly lower HL than nonsmokers and nonobese individuals. Higher HL levels were associated with higher access to health information and lower risky habits, such as physical inactivity, smoking, and alcohol consumption [60,61] These results may be explained by the strong relationship between a person's BMI or WC and lifestyle, as those with low HL are more likely to have unhealthy diets and behaviors [62]. Evidence suggests that effective interventions to improve HL in community settings may be to change unhealthy lifestyles (e.g., smoking, diet, and physical activity) [63]. Therefore, we recommend that rural health professionals be trained to properly perform WC measurements and consider them an important and more accurate “vital sign” in screening goals to improve HL [64].
Strengths and limitations
This is the first study to examine the HL among the Wa and its influencing factors. However, there are also several limitations. First, our study data came from the Wa ethnic group along the China-Burma border. Some compounding factors, such as economic, cultural, geographic environment, and health status conditions, were not taken into account; therefore, our results should be examined and carefully generalized to other studies from different cultural backgrounds. Second, the limited Chinese language proficiency of our participants may have influenced their responses to the HL questionnaire. Therefore, further research on HL among ethnic minorities should consider using different data collection methods than survey studies. Finally, the data were measured by using a screening scale from a survey and thus it is difficult to avoid recall bias.