Institutionalized data quality assessments: a critical pathway to improving the accuracy of integrated disease surveillance data in Sierra Leone

Immunization data quality and decision making in pertussis outbreak management in southern Ethiopia: a cross sectional study

Background

The aim of this study was to investigate the quality of immunization data and monitoring systems in the Dara Malo District (Woreda) of the Gamo Administrative Zone, within the Southern Nations, Nationalities, and Peoples’ Region (SNNPR) of Ethiopia.

Methods

A cross-sectional study was conducted from August 4 to September 27, 2019, in Dara Malo District. The district was purposively selected during the management of a pertussis outbreak, based on a hypothesis of ‘there is no difference in reported and recounted immunization status of children 7 to 23 months in Dara Malo District of Ethiopia’. The study used the World Health Organization (WHO) recommended Data Quality Self-Assessment (DQS) tools. The accuracy ratio was determined using data from routine Expanded Program of Immunization (EPI) and household surveys. Facility data spanning the course of 336 months were abstracted from EPI registers, tally sheets, and monthly routine reports. In addition, household surveys collected data from caretakers, immunization cards, or oral reports. Trained DQS assessors collected the data to explore the quality of monitoring systems at health posts, health centers, and at the district health office level. A quality index (QI) and proportions of completeness, timeliness, and accuracy ratio of the first and third doses of pentavalent vaccines and the first dose of measles-containing vaccines (MCV) were formulated.

Results

In this study, facility data spanning 336 months were extracted. In addition, 595 children aged 7 to 23 months, with a response rate of 94.3% were assessed and compared for immunization status, using register and immunization cards or caretakers’ oral reports through the household survey. At the district level, the proportion of the re-counted vaccination data on EPI registers for first dose pentavalent was 95.20%, three doses of pentavalent were 104.2% and the first dose of measles was 98.6%. However, the ratio of vaccination data compared using tallies against the reports showed evidence of overreporting with 50.8%, 45.1%, and 46.5% for first pentavalent, third pentavalent, and the first dose of measles vaccinations, respectively. The completeness of the third dose of pentavalent vaccinations was 95.3%, 95.6%, and 100.0% at health posts, health centers, and the district health office, respectively. The timeliness of the immunization reports was 56.5% and 64.6% at health posts and health centers, respectively, while the district health office does not have timely submitted on time to the next higher level for twelve months. The QI scores ranged between 61.0% and 80.5% for all five categories, namely, 73.0% for recording, 71.4% for archiving and reporting, 70.4% for demographic information, 69.7% for core outputs, and 70.4% for data use and were assessed as suboptimal at all levels. The district health office had an emergency preparedness plan. However, pertussis was not on the list of anticipated outbreaks.

Conclusion

Immunization data completeness was found to be optimal. However, in the study area, the accuracy, consistency, timeliness, and quality of the monitoring system were found to be suboptimal. Therefore, poor data quality has led to incorrect decision making during the reported pertussis outbreak management. Availing essential supplies, including tally sheets, monitoring charts, and stock management tools, should be prioritized in Daro Malo District. Enhancing the capacity of healthcare providers on planning, recording, archiving, and reporting, analyzing, and using immunization data for evidence-based decision making is recommended. Improving the availability of recording and reporting tools is also likely to enhance the data accuracy and completeness of the community health information system. Adapting pertussis outbreak management guidelines and conducting regular data quality assessments with knowledge sharing events to all stakeholders is recommended.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13690-022-00805-6.

An automated data cleaning method for Electronic Health Records by incorporating clinical knowledge

BACKGROUND

The use of Electronic Health Records (EHR) data in clinical research is incredibly increasing, but the abundancy of data resources raises the challenge of data cleaning. It can save time if the data cleaning can be done automatically. In addition, the automated data cleaning tools for data in other domains often process all variables uniformly, meaning that they cannot serve well for clinical data, as there is variable-specific information that needs to be considered. This paper proposes an automated data cleaning method for EHR data with clinical knowledge taken into consideration.

METHODS

We used EHR data collected from primary care in Flanders, Belgium during 1994-2015. We constructed a Clinical Knowledge Database to store all the variable-specific information that is necessary for data cleaning. We applied Fuzzy search to automatically detect and replace the wrongly spelled units, and performed the unit conversion following the variable-specific conversion formula. Then the numeric values were corrected and outliers were detected considering the clinical knowledge. In total, 52 clinical variables were cleaned, and the percentage of missing values (completeness) and percentage of values within the normal range (correctness) before and after the cleaning process were compared.

RESULTS

All variables were 100% complete before data cleaning. 42 variables had a drop of less than 1% in the percentage of missing values and 9 variables declined by 1-10%. Only 1 variable experienced large decline in completeness (13.36%). All variables had more than 50% values within the normal range after cleaning, of which 43 variables had a percentage higher than 70%.

CONCLUSIONS

We propose a general method for clinical variables, which achieves high automation and is capable to deal with large-scale data. This method largely improved the efficiency to clean the data and removed the technical barriers for non-technical people.

Clinical, epidemiological aspects, and trends of Hepatitis B in Brazil from 2007 to 2018

Hepatitis B virus (HBV) infection is a concern for public health due to its high prevalence, high infectivity, morbidity, and mortality worldwide. Brazil presents a low HBV prevalence, but has considerable heterogeneity among its geographic regions. Here, we describe the epidemiological profile of HBV infection in different regions of Brazil during 2007-2018, as well as the historical trends associated with the infection. We conducted an observational, ecological time-series study using secondary data collected from the National Notifiable Diseases Information System (SINAN). Our findings suggest that HBV infection was more likely to occur in young, sexually active adults. Individuals from Northeast and Midwest regions were more likely to present acute HBV infection, while individuals from South region were more likely to present chronic HBV infection, reinforcing that specific strategies are required for each particular region. Additionally, we observed a general decreasing trend of infection starting in 2014, however there was an increasing trend of infection in men and in individuals over 40 years old. Although we observed a decreasing trend in HBV infection, active surveillance is needed to prevent HBV spread and possible epidemics, as well as encouraging the vaccination of adults, especially young adult males. Our findings can inform the conduct of large-scale observational studies to evaluate clinical, economical, and social impacts of HBV infections, leading to improved social policies. Finally, our results highlight the need to improve data quality and completeness of epidemiological data, minimizing eventual errors that can make prevention and control strategies difficult.