Health management information system (HMIS) data verification: A case study in four districts in Rwanda

Abstract

Introduction Reliable Health Management and Information System (HMIS) data can be used with minimal cost to identify areas for improvement and to measure impact of healthcare delivery. However, variable HMIS data quality in low- and middle-income countries limits its value in monitoring, evaluation and research. We aimed to review the quality of Rwandan HMIS data for maternal and newborn health (MNH) based on consistency of HMIS reports with facility source documents. Methods We conducted a cross-sectional study in 76 health facilities (HFs) in four Rwandan districts. For 14 MNH data elements, we compared HMIS data to facility register data recounted by study staff for a three-month period in 2017. A HF was excluded from a specific comparison if the service was not offered, source documents were unavailable or at least one HMIS report was missing for the study period. World Health Organization guidelines on HMIS data verification were used: a verification factor (VF) was defined as the ratio of register over HMIS data. A VF<0.90 or VF>1.10 indicated over- and under-reporting in HMIS, respectively. Results High proportions of HFs achieved acceptable VFs for data on the number of deliveries (98.7%;75/76), antenatal care (ANC1) new registrants (95.7%;66/69), live births (94.7%;72/76), and newborns who received first postnatal care within 24 hours (81.5%;53/65). This was slightly lower for the number of women who received iron/folic acid (78.3%;47/60) and tested for syphilis in ANC1 (67.6%;45/68) and was the lowest for the number of women with ANC1 standard visit (25.0%;17/68) and fourth standard visit (ANC4) (17.4%;12/69). The majority of HFs over-reported on ANC4 (76.8%;53/69) and ANC1 (64.7%;44/68) standard visits. Conclusion There was variable HMIS data quality by data element, with some indicators with high quality and also consistency in reporting trends across districts. Over-reporting was observed for ANC-related data requiring more complex calculations, i.e., knowledge of gestational age, scheduling to determine ANC standard visits, as well as quality indicators in ANC. Ongoing data quality assessments and training to address gaps could help improve HMIS data quality.

Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania

“…Despite the fact that the HMIS is the backbone for strong health systems, studies in Sub-Saharan Africa (SSA) have reported challenges with data quality, including completeness and timeliness, accuracy, consistence and poor utilization of HMIS tools [1,[5][6][7][8][9][10][11][12][13]. The concerns about the quality of routine information have undermined data utilization for decision-making in the health sector [9,10,[14][15][16][17][18][19][20]. Completeness and timeliness entails completeness of reports, completeness of data and timeliness of reports; while consistency refers to accuracy, outliers, trends and consistency between indicators.…”

“…Another study in Nigeria, reported that facility-reported data were incomplete by 40% of the time [18]. On the other hand, internal data inconsistency is quite common in a number of countries in Sub-Saharan Africa [18,19,21]. Both underand over-reporting have been frequently observed, and it varied across indicators, facilities and districts [18,22].…”

Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania

“…Another study in Nigeria, reported that facility-reported data were incomplete by 40% of the time [18]. On the other hand, internal data inconsistency is quite common in a number of countries in Sub-Saharan Africa [18][19]21]. Both under-and overreporting have been frequently reported, and it varied across indicators, facilities and districts [18,22].…”

“…Both under-and overreporting have been frequently reported, and it varied across indicators, facilities and districts [18,22]. For instance, in a study in Rwanda, over-reporting was observed for ante-natal care-related data than for other indicators [19]. In some cases, missing values, measurement error, inaccuracy and false reports from unidenti ed source have been observed [20].…”

Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania

“…Another study in Nigeria, reported that facility-reported data were incomplete by 40% of the time [18]. On the other hand, internal data inconsistency is quite common in a number of countries in Sub-Saharan Africa [18][19]21]. Both under-and overreporting have been frequently observed, and it varied across indicators, facilities and districts [18,22].…”