Back

Protocol of the study for Setting up of a system for "Medical Certification of Cause of Death" for non-institutional deaths in a selected area of a Taluk of Kolar District, Karnataka, India: feasibility and validity

Muralidhar, M.; Rangamani, S.; Kulothungan, V.

2025-02-06 epidemiology
10.1101/2025.02.05.25321716 medRxiv
Show abstract

RationaleThe current coverage of "Medical Certification of Cause of Death" in India is only 22.5%. This is largely due to significant proportion of deaths occurring outside the hospitals (non-institutional deaths). The "cause of death" in such cases is unlikely to be certified by any doctor. The present study attempts to address this gap by developing a system of MCCD for "non-institutional deaths" in the country. NoveltyThis is a first of its kind study attempting to address the gap of coverage as well as reliability of MCCD for "non-institutional deaths". ObjectivesTo assess the feasibility of "Physician derived Cause of death" approach for deducing "cause of death" in "non-institutional deaths" in a selected area of Kolar Taluk, Karnataka and to validate this approach MethodsDoctors of selected major hospitals in Kolar taluk and PHC medical officers and private practitioners of 2 selected PHC areas of the taluk would be trained in arriving at "Cause of Death" in "non-institutional deaths" using the "PhyCoD" tool. The "cause of death" deduced by this approach would be validated against the gold standard autopsy wherever possible. The approach will also be tested for "inter-rater reliability". Expected outcomeO_LIDevelopment of a tool for physicians for deducing "Cause of Death" in "non-institutional deaths" C_LIO_LIIncreased coverage of MCCD for "non-institutional deaths" C_LIO_LIImproved accuracy in the reporting of "cause of death" for "non-institutional deaths" C_LIO_LIReduced delay in the reporting of "cause of death" for "non-institutional deaths" C_LI

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.