Data for Program Management: An Accuracy Assessment of Data Collected in Household Registers by Community Health Workers in Southern Kayonza, Rwanda

Abstract

Community health workers (CHWs) collect data for routine services, surveys and research in their communities. However, quality of these data is largely unknown. Utilizing poor quality data can result in inefficient resource use, misinformation about system gaps, and poor program management and effectiveness. This study aims to measure CHW data accuracy, defined as agreement between household registers compared to household member interview and client records in one district in Eastern province, Rwanda. We used cluster-lot quality assurance sampling to randomly sample six CHWs per cell and six households per CHW. We classified cells as having 'poor' or 'good' accuracy for household registers for five indicators, calculating point estimates of percent of households with accurate data by health center. We evaluated 204 CHW registers and 1,224 households for accuracy across 34 cells in southern Kayonza. Point estimates across health centers ranged from 79 to 100% for individual indicators and 61 to 72% for the composite indicator. Recording error appeared random for all but the widely under-reported number of women on modern family planning method. Overall, accuracy was largely 'good' across cells, with varying results by indicator. Program managers should identify optimum thresholds for 'good' data quality and interventions to reach them according to data use. Decreasing variability and improving quality will facilitate potential of these routinely-collected data to be more meaningful for community health program management. We encourage further studies assessing CHW data quality and the impact training, supervision and other strategies have on improving it.

Data Quality Methods and Applications in Health Care System: A Systematic Literature Review

“…Among the appropriately quantitative approaches, it is useful to analyse the treatment of Mitsunaga et al (2015), which provides a focus on the accuracy of data in household registries by health workers in Rwanda. The study envisaged the possibility of creating a data quality assessment system based on indicators related to demography and women's health.…”

“…To this end the results belonging to the collections of Taylor & Francis (Bai et al, 2018), BioMed Central (Ledikwe et al, 2014), Springer (Mitsunaga et al, 2015), Journal of Health Management & Informatics (Firouraghi et al, 2018), BioMed Central (Skyttberg et al, 2016), Elsevier (Verma, 2014), Oxford University Press (Watson et al, 2017), American College of Healthcare Executives (Wills, 2014) and National Institutes of Health (NIH) Health Care Systems Research Collaboratory (Zozus M. N., et al, 2014).…”

“…42 2. monitoring of clinical trial operations in real time (e.g., through sample volume and time from sample collection to laboratory reception); 3. the insertion of data at several collection points in each site (e.g., Laboratory, clinic, first aid); 4. rapid implementation of any changes to the information system required by the clinical staff.The validation of the system has provided for a training activity aimed at homogenizing data insertion in the database and monthly monitoring systems on the lack of data.The quantitative cases also examined partially meet the criteria outlined by the World Health Organization. Completeness and timeliness is analysed by Mitsunaga et al (2015) as an essential element during the home visits of health personnel and the compilation of digital folders, in the case of Watson et al (2017) refers exclusively to the completeness referred to demographic data, clinical vaccinations, environmental elements, and risk factors.…”

“…The quantitative cases also examined partially meet the criteria outlined by the World Health Organization. Completeness and timeliness is analysed by Mitsunaga et al (2015) as an essential element during the home visits of health personnel and the compilation of digital folders, in the case of Watson et al (2017) refers exclusively to the completeness referred to demographic data, clinical vaccinations, environmental elements, and risk factors.…”

Improving Maternal Health in Rwanda: The Role of Community-Based Interventions: A Systematic Review (2005-2015)

“…Mitsunaga et al reported an overall 'largely good' accuracy of the DSA across lowest administrative units (known as cells), with varying results by indicator. From their findings, there is potential for the routinely-collected data by CHWs to be meaningful inputs for strengthening the community health program management [15,16].…”