Spatial Distribution of the Quality of COVID-19 Mortality Data in Salvador, Bahia
1
Institute of Collective Health, Federal University of Bahia, Salvador, Brazil
2
School of Public Health, University of São Paulo, São Paulo, Brazil
3
Municipal Health Secretariat, Salvador, Brazil
4
Florida Christian University, Florida, United States
Popul. Med. 2026;8(Supplement Supplement 1):A3844
ABSTRACT
BACKGROUND:
High-quality data are crucial for making effective healthcare decisions and improving patient care. Data quality became even more vital during the pandemic. This study focuses on assessing the quality of COVID-19 mortality data in the Mortality Information System (SIM) in Salvador. The objective was to evaluate this quality across various geographical levels, including districts, borough governments, neighborhoods, and healthcare facilities.
METHODS:
An ecological study was conducted using SIM data, analyzing the years 2020 and 2021. Deaths were classified by their causes, and the Data Quality Index (DQI) was calculated. The DQI measured quality by considering attributes such as completeness, consistency, validity, and timeliness. Data were consolidated using Power BI software to allow for detailed analyses, and different quality indices were considered for distinct geographic areas.
RESULTS:
The study revealed improvements in the quality of COVID-19 mortality data throughout the pandemic years in Salvador. The overall index increased, mainly due to enhancements in the "Validity" dimension. However, significant differences in data quality were identified among different geographical areas.
CONCLUSIONS:
While overall quality improved, some areas exhibited persistent challenges related to limited access to healthcare services and socioeconomic inequalities. Addressing these spatial disparities is essential to ensure equitable health surveillance and effective resource allocation in future health emergencies.
High-quality data are crucial for making effective healthcare decisions and improving patient care. Data quality became even more vital during the pandemic. This study focuses on assessing the quality of COVID-19 mortality data in the Mortality Information System (SIM) in Salvador. The objective was to evaluate this quality across various geographical levels, including districts, borough governments, neighborhoods, and healthcare facilities.
METHODS:
An ecological study was conducted using SIM data, analyzing the years 2020 and 2021. Deaths were classified by their causes, and the Data Quality Index (DQI) was calculated. The DQI measured quality by considering attributes such as completeness, consistency, validity, and timeliness. Data were consolidated using Power BI software to allow for detailed analyses, and different quality indices were considered for distinct geographic areas.
RESULTS:
The study revealed improvements in the quality of COVID-19 mortality data throughout the pandemic years in Salvador. The overall index increased, mainly due to enhancements in the "Validity" dimension. However, significant differences in data quality were identified among different geographical areas.
CONCLUSIONS:
While overall quality improved, some areas exhibited persistent challenges related to limited access to healthcare services and socioeconomic inequalities. Addressing these spatial disparities is essential to ensure equitable health surveillance and effective resource allocation in future health emergencies.
| eISSN: | 2654-1459 |
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