Abstract:
Background: Complete, timely and accurate public health information is essential for monitoring health and for improving the delivery of health-care services. Studies of public Health Management Information Systems (HMIS) in resource-poor countries frequently document problems with data quality, such as incomplete records and untimely reporting. Yet these systems are often the only data sources available for the continuous, routine monitoring of health programmes in the districts.
Funding and support for public health activities, such as maternal and child health (MCH) services, depends on demonstrating coverage using routine data. Assessment of nutritional status is one of the services offered at MCH clinics where data is also routinely collected. The quality of nutrition surveillance data from MCH clinics under the health information system remains a challenge. This study, therefore, aimed at assessing the quality and factors associated with nutrition status surveillance data quality collected at health facilities under the HMIS.
Methods: A cross-sectional study design was conducted in 20 health facilities of Arusha City from October 2013 to February 2014. These health facilities were selected using a stratified random sampling technique from 67 health facilities of the council. Documentary review was conducted to obtain information on data quality. Observation checklist and interviews were used to collect data on determinants of data quality. Data were analyzed using Epi Info computer software. Descriptive statistics was done to summarize the characteristics of study participants and facilities. Regression analysis was performed to determine independent factors associated with quality of data and presented as AOR at 95% CI. A p-value < 0.05 is considered as statistically significant.
Results: A total of 99 respondents from 20 health facilities in Arusha City were interviewed. About 87.0% (86/99) of the participants were female. The mean age of respondents was 37 (SD 9.68) years. On average the level of data accuracy in the health facilities was 55.1% ranging from 8.1% to 97.0% and the average completeness rate was 67.5%. The health facilities submit reports to the district on average eleven (11, SD 4.3) days after the pre-set deadline (Range: 1 to 38 days). Facilities with motivated health workers had high odds of having accurate AOR 7.0 (95%CI 3.7, 77.5) and complete data AOR 12.0 (95%CI 2.1, 69.8) compared to facilities with unmotivated workers. The availability of data collection tools increased the likelihood of having complete data (COR 2.3(3.19, 27.57)). The presence of HMIS focal person was significantly associated with data accuracy (COR 25.0(2.16, 73.37)) and completeness (COR 16.0(2.40, 61.74)). Knowledge on HMIS, perception of importance of good quality data and supportive supervision did not influence significantly data quality.
Conclusion: The quality of HMIS data for nutritional status is of modest quality and below the recommended level, that is 95% accurate, 80% complete and timely (reported before or on the pre-set deadline). Extra efforts are required to improve the quality of data for effective use in decision-making and planning in the district and facilities.
