Australia’s multicultural landscape has evolved significantly due to increasing migration. Researchers and policymakers have recognised the importance of understanding cultural diversity within the population. To conduct effective research on individuals from multicultural communities it is crucial to define and measure their cultural and linguistic status accurately. In this blog, we explore the significance of Culturally and Linguistically Diverse (CALD) variables and their impact on research outcomes.
The Australian Bureau of Statistics (ABS) recommends a standardised set of CALD measures for data collection. This minimum core set includes the following variables:
- Country of Birth (COB): Identifying the birthplace of study participants.
- Main Language Spoken at Home (other than English): Assessing language preferences.
- Proficiency in Spoken English: Evaluating language skills.
- Indigenous Status: Recognising Indigenous heritage.
These variables serve as the foundation for determining an individual’s CALD status. Additionally, researchers may collect optional variables such as COB of parents and year of arrival, as suggested by ABS in the standards.
Despite ABS recommendations, health datasets often omit certain CALD variables. Researchers may exclude variables that seem irrelevant or unnecessary for their specific study. Consequently, this approach leads to inconsistencies in CALD data collection across epidemiological studies and routinely collected datasets.
To address these challenges, we conducted a systematic review and examined which CALD variables are commonly collected in health datasets and whether they align with ABS standards.
We searched databases such as PubMed, Embase, and Cinahl, focusing on studies published from 2014 onwards. Additionally, we reviewed government reports in our grey literature, to identify relevant variables used to define the CALD population.
Our review revealed the following insights:
- Incomplete CALD variables collection: Our preliminary findings reveal that none of the health datasets fully adhere to the ABS recommended standards for CALD variables. This deficiency poses challenges when assessing the health status of CALD populations.
- Selective Variable Usage: Not all collected CALD variables are consistently used in research studies. For instance, some hospital datasets include multiple CALD variables (such as country of birth, language spoken at home, religious affiliation, proficiency in English, and interpreter requirements). However, researchers often choose to use only a subset of these variables, potentially underestimating disease burden.
- Commonly Collected Variables:
- Country of Birth: COB is frequently collected but is insufficient on its own. Studies using COB alone tend to categorise populations as either Australian-born or CALD, overlooking nuances.
- Language Variables: Language-related variables (e.g., language spoken at home) vary across datasets. Inconsistencies were observed in how the language question is framed might impact responses.
- Ethnicity and Ancestry: Researchers sometimes interchangeably use terms like ethnicity, ancestry, cultural groups, and CALD groups. Ancestry and ethnicity are often self-identified, leading to inconsistencies in data collection.
These practices in collection of CALD variables leads to underrepresentation of true CALD population. This misclassification affects disease burden estimates and hinders accurate interpretation of health data. Gaps in data collection translate to gaps in policy formulation and intervention strategies. Inadequate data may lead to ineffective or inappropriate health policies.
Comprehensive CALD data collection is essential for informed decision-making. Researchers should prioritise consistent collection of relevant variables, aligning with ABS standards. By addressing these gaps, we can bridge the divide between data and effective policy changes, ultimately improving health outcomes for all Australians. Understanding CALD variables is essential for informed research and policymaking. Researchers should strive for consistency in data collection, considering both ABS standards and study-specific needs. By doing so, we can enhance our understanding of CALD communities and promote equitable health outcomes.