Data quality and it’s correlation with Routine health information system structure and input at public health centers in Addis Ababa, Ethiopia.

  • Wondimu Ayele
  • Ephrem Biruk
  • Tigist Habtamu
  • Girma Taye
  • Mulugeta Tamire
  • Adamu Addissie

Abstract

Abstract
Background: The Government of Ethiopia, together with its partners, has made significant progress over the years in the standardization and implementation of health information system (HIS). The sector continues to be challenged by its lack of accurate, timely and thorough data, which therefore has affected the quality of care, planning and management systems in the country. This study assessed HIS for managing health care data and data quality in the Addis Abeba City Administration in Ethiopia.

Methods: A cross-sectional study was conducted to determine the quality of the data. The study was conducted in 25 health centers in Addis Ababa City. Connected woreda assessment tools have been used. Composite analysis was carried out to determine the implementation of routine health information system structure and input. Univariate and multiple linear regression are used to identify predictors of overall data quality,reporting findings using a regression coefficient and 95 % confidence interval.

Result: The overall |implementation of RHIS structure and input was 63.9% at health facilities. The mean score of RHIS structure and input was 19.2/30 + 4.7. The overall data quality was found to be 57.9% with a 95 Confidence interval of (95%CI (51.0-64.9%). Overall data accuracy, completeness, and timeliness in all assessed health facilities was 69.6% (95 IC 59.8-79.3%), 49.5% (95 CI 38.3-60.7%), and 56% (95 CI, 48.8_63.2), respectively. Supportive supervision and mentorship found to be associated to data quality, as supervision mean score increase by one-unit data quality increases by 1.42 with 95% CI (0.10-2.76) given another variable held constant.

Conclusion and recommendation: Overall data quality was much lower than the national acceptable level of less than 90%. Supportive supervision and mentorship has a significant correlation with data quality. A considerable number of health facilities have not yet fulfilled all the input required to strengthen the HIS. Strengthen support supervision and mentorship is an opportunity to improve data quality at the level of health facilities. [Ethiop. J. Health Dev. 2021; 35(SI-1):33 - 41]

Keywords: Data quality, RHIS structure and input, healthcare data

Published
2021-07-16

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eISSN: 1021-6790