Cross-cultural Adaptation and Psychometric Validation of the Hindi version of Multimorbidity Assessment Questionnaire for Primary Care (MAQ-PC) among Rural Older Adults in North India

As the world's population ages, a quiet but profound shift is occurring in how people experience health. In the past, older adults often faced a single, acute illness that could be treated and resolved. Today, it is increasingly common for an older person to live with several chronic conditions at the same time, a situation doctors call multimorbidity. This is not merely a matter of having a list of diagnoses; it is a complex web where conditions interact, affecting a person's ability to function, their mental well-being, and their quality of life. To help doctors and researchers understand this complexity, they need tools that can measure it accurately. However, a tool designed for one culture or language does not always work in another. Words that describe pain or sadness in English might not carry the same meaning or emotional weight when spoken in Hindi, especially among older adults living in rural villages where daily life and medical experiences differ from urban centers. Without a version of these tools that feels natural and clear to the people using them, the data collected can be misleading, leaving a significant gap in how we understand and care for the aging population in India.

In a recent study, researchers set out to fill this gap by creating and testing a Hindi version of a specific tool called the Multimorbidity Assessment Questionnaire for Primary Care, known as MAQ-PC. This questionnaire was originally developed in English to help doctors in India get a full picture of an older patient's health, covering not just their physical diseases but also their mental health and how well they can manage daily tasks. The researchers, working out of the All India Institute of Medical Sciences in Gorakhpur, focused on rural communities in northern India, a region where Hindi is the primary language. Their goal was not simply to translate the words from English to Hindi, but to adapt the entire experience of the questionnaire so that it would make sense to a farmer or a homemaker in a village. They began by having two independent translators convert the English questions into Hindi, then brought in a panel of experts, including doctors, a psychologist, and a language specialist, to review the drafts. These experts ensured that the questions captured the same ideas as the original, even if the words had to change to fit local customs. For instance, to describe the feeling of being down or hopeless, they chose phrases that rural elders would use in everyday conversation, and they clarified medical terms like arthritis with local words for joint pain. Before the final version was locked in, a small group of older adults tested the questionnaire, reading through it and telling the researchers if any part felt confusing or unnatural.

Once the questionnaire was refined, the team put it to the test with a large group of 340 older adults, all aged 60 or older, living in the rural areas around Gorakhpur. These participants were interviewed face-to-face in their homes or at local clinics. The researchers wanted to see if the new Hindi version was reliable, meaning if it gave consistent results, and valid, meaning if it actually measured what it was supposed to measure. They looked closely at how the participants answered the questions to see if the different parts of the questionnaire held together logically. The results showed that the tool worked well. The questions about depression and quality of life were particularly strong, showing that the Hindi version could reliably capture these sensitive and important aspects of health. The overall structure of the questionnaire also held up, revealing that it successfully grouped health issues into distinct categories, such as physical functioning, emotional well-being, and specific types of chronic diseases like diabetes or heart conditions. The analysis confirmed that the tool could distinguish between these different areas of health, just as the original English version did.

The study also found that the questionnaire was culturally appropriate and easy for the participants to understand. When the researchers asked the older adults about their experience with the questions, the feedback was overwhelmingly positive. The participants found the language clear and the topics relevant to their lives. This was a crucial finding because it meant that the tool was not just a translation, but a bridge that connected medical concepts to the lived reality of rural Indians. The researchers noted that while some sections of the questionnaire, like the list of chronic diseases, were naturally less consistent because they were just checklists of different conditions, the core parts that measured how a person felt and functioned were robust. This distinction is important because it tells us that the tool is doing exactly what it was designed to do: separating the simple count of diseases from the complex experience of living with them.

By successfully adapting this tool, the researchers have provided a new way for doctors and scientists in India to study the health of older adults in a way that respects their language and culture. The study did not claim to have solved the problem of multimorbidity, nor did it suggest that this single tool is perfect for every situation. Instead, it offered a solid, tested foundation. The researchers acknowledged that their work was done in one specific region, and that future studies would need to check if the tool works just as well in cities or in other parts of the country. They also noted that they had not yet tested how stable the answers were over time, which is a necessary step for any medical tool. However, the work done here establishes that the Hindi version of the Multimorbidity Assessment Questionnaire is a trustworthy instrument. It allows for a clearer, more accurate view of the health challenges facing older adults in rural India, paving the way for better care and more informed health policies that are built on data that truly reflects the people it is meant to serve.