Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Data Quality and the Role of Metadata Data Quality
Reliable data are essential for credible environmental health efforts. Since agen- cies rely on various sources, data quality is crucial for effective analysis and de - cision-making. Inaccurate data, be it incomplete, outdated, or inconsistent, can lead to false conclusions and missed opportunities for action. Therefore, the goal is for data to be accurate, complete, consistent, and timely. EPH professionals can evaluate datasets using four key dimensions: 1) accuracy, 2) completeness, 3) consistency, and 4) timeliness. These criteria help determine if data can be trusted to inform programs, policies, and public communications.
Table 5 provides examples to illustrate how each data quality dimension plays out in practice.
Table 5
DATA QUALITY DIMENSION
DEFINITION
EXAMPLE
Accuracy
Does the data accurately reflect real-world conditions?
A health department installs calibrated air monitors near a school. If the sensor reads 35 µg/m³ on a visibly clear day, it could be an inaccurate reading. Investigating sensor drift ensures valid results. Missing GPS data in inspection records prevents mapping violation hotspots, limiting enforcement strategies. One hospital codes asthma emergency department visits as “respiratory illness,” while others use ICD-10. Inconsistent coding makes state-level comparisons unreliable.
Completeness
Are there missing fields or gaps that could distort the analysis? Are data collection methods and definitions uniform across time and systems? Are the data recent enough to support timely decisions?
Consistency
Timeliness
Using 2019 census data for 2025 can lead to screening misses of recent demographic shifts, potentially overlooking high-risk populations.
8
Powered by FlippingBook