Millions in Bangladesh speak Sylheti and Chittagonian (Chatgaiyya) dialects, yet most public health guidance exists only in Standard Bangla, which creates barriers and safety risks. Ad-hoc translation further harms comprehension, while challenges such as scarce data, non-standard spelling, medical terms, numerals, and idioms make accurate translation difficult. We present BanglaCHQ-Prantik, the first benchmark for this setting, extending BanglaCHQ-Summ with human gold references from 17 native translators. We evaluate Qwen 2.5 3B, Gemma 3 1B, GPT-4o mini, and Gemini 2.5 Flash under zero-shot, one-shot, five-shot, and chain-of-thought prompts, using BLEU, ROUGE-1/2/L, and METEOR. Closed-source models (GPT-4o, Gemini 2.5) lead overall, with Gemini 2.5 Flash being strongest. Few-shot prompting helps especially for Sylheti, though errors persist with terminology, numerals, and idioms. The dataset is designed to support both NLP research and public health communication by enabling reliable translation across regional Bangla dialects. To our knowledge, this is the first medical-domain dataset for Sylheti/Chittagonian.