Sentence examples for Inadequate data quality from inspiring English sources

Inadequate data quality may impair our understanding of the true epidemiology of TB, compromise core program functions, and undermine our ability to meet program objectives and goals.

Inadequate data quality does not account for missing these two base changes (UI20291 and NS3471.9) in 8-fold pools, because the gel images were typical of what is seen in our ATP operation.

In the final analyses, one score was excluded, being a T2 t0-value (0.7 %; replaced with the T1 t0-value) of inadequate data quality.

However, two general hospitals dropped out from the study due to insufficient infection control personnel and one general hospital was excluded due to inadequate data quality.

This was due to a variety of reasons, including doubts about the benefits of the SCR, insufficient reimbursement, competing priorities, inadequate data quality, incompatibility of their software system, and ethical concerns.

The Viet Nam Statistical Strategy has identified several shortcomings in the Viet Nam system, including a lack of organizational and human resources to reform the system, indicator systems in data collection that are not comprehensive, inadequate data quality, and the late dissemination of data, which reduces its utilization.

These past studies were limited by inadequate adjustments [ 59], loss of data quality due to the heterogeneity of the population (not specific to T2D), categorization of continuous outcome data and data-driven analysis [ 60, 61].

Four trials aimed to report quality of life (QoL) but inadequate data prevented pooling of the results.

Inadequate presentation of performance, lack of confidence in data quality and the risk of adverse consequences are mentioned as reasons [ 14, 16].

In addition, fragmented data collection practices and silos of the HIS, inconsistent data quality and validity together with inadequate allocation of resources continued to affect the data collection process.

HMIS are faced by significant constraints including: inadequate financial and human resources; limited attention paid to data quality assurance and supervisory support; and failure to maximize the potential of emerging information technologies.