iCCM data quality: an approach to assessing iCCM reporting systems and data quality in 5 African countries

Abstract

Background Ensuring the quality of health service data is critical for data-driven decision-making. Data quality assessments (DQAs) are used to determine if data are of sufficient quality to support their intended use. However, guidance on how to conduct DQAs specifically for community-based interventions, such as integrated community case management (iCCM) programs, is limited. As part of the World Health Organization’s (WHO) Rapid Access Expansion (RAcE) Programme, ICF conducted DQAs in a unique effort to characterize the quality of community health worker-generated data and to use DQA findings to strengthen reporting systems and decision-making. Methods We present our experience implementing assessments using standardized DQA tools in the six RAcE project sites in the Democratic Republic of Congo, Malawi, Mozambique, Niger, and Nigeria. We describe the process used to create the RAcE DQA tools, adapt the tools to country contexts, and develop the iCCM DQA Toolkit, which enables countries to carry out regular and rapid DQAs. We provide examples of how we used results to generate recommendations. Results The DQA tools were customized for each RAcE project to assess the iCCM data reporting system, trace iCCM indicators through this system, and to ensure that DQAs were efficient and generated useful recommendations. This experience led to creation of an iCCM DQA Toolkit comprised of simplified versions of RAcE DQA tools and a guidance document. It includes system assessment questions that elicit actionable responses and a simplified data tracing tool focused on one treatment indicator for each iCCM focus illness: diarrhea, malaria, and pneumonia. The toolkit is intended for use at the national or sub-national level for periodic data quality checks. Conclusions The iCCM DQA Toolkit was designed to be easily tailored to different data reporting system structures because iCCM data reporting tools and data flow vary substantially. The toolkit enables countries to identify points in the reporting system where data quality is compromised and areas of the reporting system that require strengthening, so that countries can make informed adjustments that improve data quality, strengthen reporting systems, and inform decision-making.

A conceptual framework for measuring community health workforce performance within primary health care systems

“…Assessment of data use may require special cross-sectional and ethnographic studies, to understand whether data are being used for decision-making and assess reasons for data use/disuse behaviors. Inclusion of this metric in the framework also highlights the need to have management structures in place such that those that are collecting the data, especially client data, also have access to that information [46].…”

Quality of malaria data in public health facilities in three provinces of Mozambique

“…The fact that there were significant differences in the quality of data collected at different wards within facilities suggests there are instances where clinicians are paying more attention to the quality of the data they record than others or that there are cadres who record more accurately than others. Whether this is due to training and supervision visits such has been seen in community health worker DQAs [5,6], the presence of guidance materials, the size of the facility [15], or other factors could not be determined through this study. Further investigation of these findings could, however, provide guidance about how to improve data quality, for example, optimizing the type/frequency of in-service trainings, and ensuring proper monitoring and evaluation standard operating procedures are visible and available.…”

“…Previous studies in Mozambique have highlighted problems with malaria data quality collected at public health facilities and community health workers, including frequent stockouts of standardized registries, discrepancies between registry and summary aggregate data forms, and lack of consistent definitions for suspected and confirmed cases [5,6]. While most efforts to evaluate data quality have focused on errors with aggregating patient information and practices of health care workers in Mozambique.…”

Developing a systematic approach to assessing data quality in secondary use of clinical data based on intended use

“…Separately, numerous tools to evaluate specific aspects of DQ in a research network, data system, or healthcare organization have been published or made available 21,28‐42 . For example, Pezoulas et al apply statistical approaches to produce curated datasets accompanied by reports summarizing and visualizing detected problematic fields.…”