Introduction
Indonesia has not optimally provided complete and reliable civil registration and vital statistics (CRVS). Death certification is one of the elements of the CRVS system. Reliable data on death rates and causes serve as the basis for building a strong evidence base for public health policy, planning, monitoring, and evaluation. This study aims to implement an approach to identifying the cause of death through verbal autopsy by empowering community health workers during the pandemic.
Method
This study is implementation research with the empowerment of the community, in this case, health cadres and health facilitators/workers, to identify the cause of death through a mobile-based verbal autopsy. This implementation research consisted of four main activities: community-based verbal autopsy, mobile-based verbal autopsy development, data collection, and analysis of the suspected causes of death using InterVA-5.
Result
From October to November 2020, a total of 143 respondents were willing to do a verbal autopsy interview (response rate of 58%). Of 143 respondents, most of them were women (112 or 78.3%), was the child of the deceased (61 or 42.7%) and lived with the deceased until before he/she died (120 or 83.9%). Based on the characteristics of the deceased, of 143 deceased, 78 (54.5%) were male, 134 (93.7%) were adults, 100 (69.9%) died at home, and 119 (83.2%) did not have a death certificate stating the cause of death. The cause of death of 143 deceased mainly was infectious disease (92 or 64.3%), followed by non-communicable disease (39 or 27.3%), external factors (5 or 3.5%), and unknown factors (4 or 2.8%). In sequence, the top five suspected causes of death are acute respiratory infection, including pneumonia (72 or 50.3%), other and unspecified infectious disease (18 or 12.6%), other and unspecified cardiac disease (17 or 11.9%), acute cardiac disease (4 or 2.8%), and Digestive neoplasms (4 or 2.8%).
Conclusion
The findings showed that the mobile-based verbal autopsy using a community-based mechanism was feasible during the COVID-19 pandemic.