Quality of routine health facility data used for newborn indicators in low- and middle-income countries: A systematic review

Abstract

Background High-quality data are fundamental for effective monitoring of newborn morbidity and mortality, particularly in high burden low- and middle-income countries (LMIC). Methods We conducted a systematic review on the quality of routine health facility data used for newborn indicators in LMIC, including measures employed. Five databases were searched from inception to February 2021 for relevant observational studies (excluding case-control studies, case series, and case reports) and baseline or control group data from interventional studies, with no language limits. An adapted version (19-point scale) of the Critical Appraisal Tool to assess the Quality of Cross-Sectional Studies (AXIS) was used to assess methodological quality, and results were synthesized using descriptive analysis. Results From the 19 572 records retrieved, 34 studies in 16 LMIC countries were included. Methodological quality was high (>14/19) in 32 studies and moderate (10-14/19) in two. Studies were mostly from African (n = 30, 88.2%) and South-East Asian (n = 24, 70.6%) World Health Organization (WHO) regions, with very few from Eastern Mediterranean (n = 2, 5.9%) and Western Pacific (n = 1, 2.9%) ones. We found that only data elements used to calculate neonatal indicators had been assessed, not the indicators themselves. 41 data elements were assessed, most frequently birth outcome. 20 measures of data quality were used, most along three dimensions: 1) completeness and timeliness, 2) internal consistency, and 3) external consistency. Data completeness was very heterogeneous across 26 studies, ranging from 0%-100% in routine facility registers, 0%-100% in patient case notes, and 20%-68% in aggregate reports. One study reported on the timeliness of aggregate reports. Internal consistency ranged from 0% to 96.2% in four studies. External consistency (21 studies) varied widely in measurement and findings, with specificity (6.4%-100%), sensitivity (23.6%-97.6%), and percent agreement (24.6%-99.4%) most frequently reported. Conclusions This systematic review highlights a gap in the published literature on the quality of routine LMIC health facility data for newborn indicators. Robust evidence is crucial in driving data quality initiatives at national and international levels. The findings of this review indicate that good quality data collection is achievable even in high-burden LMIC settings, but more efforts are needed to ensure uniformly high data quality for neonatal indicators.

Quality of maternal healthcare and travel time influence birthing service utilisation in Ghanaian health facilities: a geographical analysis of routine health data

“…14 This is because the data can be incomplete, difficult to access, and expensive to extract and process. 15 16 Most studies conduct one-off subnational surveys to collect data 17 18 or rely on national household surveys such as the Demographic and Health Survey. 19 20 However, surveys can be expensive and infrequent.…”

“…There is a paucity of studies using routine health data to evaluate the relationship between quality maternal healthcare and birthing service utilisation 14. This is because the data can be incomplete, difficult to access, and expensive to extract and process 15 16. Most studies conduct one-off subnational surveys to collect data17 18 or rely on national household surveys such as the Demographic and Health Survey 19 20.…”

It’s complicated…: Exploring the missed opportunities and reasons for non-performance of assisted vaginal births in Kenya

“…The lack of adequate filing systems and the incomplete or contradictory information that many of the sets of notes in this study contained, is likely to impact efforts to improve the quality of patient care, both directly as complete medical history is not available and through difficulty in conducting clinical audit and research [12].…”

Quality of Maternal & Newborns Health indicators in Western Province of Rwanda

“…In the same study, only 6.7% indicators were found meeting all requirements for scientific soundness [7]. Systematic review of maternal and newborn data from 34 studies highlighted heterogeneity [8]. poor skills are all shown to be linked issues [11].…”

“…Once data are reported with poor quality, they contribute to inadequate decisions taken at different levels, causes insufiscient confidence in health systems, and result in non-validity of impact evaluation studies. Findings from 34 studies in published literature evaluated the quality of newborn data collected at health facility level in LMIC, revealed that there is no study reporting on the quality of newborn indicators and the systematic review done highlighted heterogeneity [17]. In Rwanda, maternal health related data was over-reported more than other indicators [18].…”