Effects of a health information system data quality intervention on concordance in Mozambique: time-series analyses from 2009–2012

Abstract

BackgroundWe assessed the effects of a three-year national-level, ministry-led health information system (HIS) data quality intervention and identified associated health facility factors.MethodsMonthly summary HIS data concordance between a gold standard data quality audit and routine HIS data was assessed in 26 health facilities in Sofala Province, Mozambique across four indicators (outpatient consults, institutional births, first antenatal care visits, and third dose of diphtheria, pertussis, and tetanus vaccination) and five levels of health system data aggregation (daily facility paper registers, monthly paper facility reports, monthly paper district reports, monthly electronic district reports, and monthly electronic provincial reports) through retrospective yearly audits conducted July-August 2010–2013. We used mixed-effects linear models to quantify changes in data quality over time and associated health system determinants.ResultsMedian concordance increased from 56.3% during the baseline period (2009–2010) to 87.5% during 2012–2013. Concordance improved by 1.0% (confidence interval [CI]: 0.60, 1.5) per month during the intervention period of 2010–2011 and 1.6% (CI: 0.89, 2.2) per month from 2011–2012. No significant improvements were observed from 2009–2010 (during baseline period) or 2012–2013. Facilities with more technical staff (aβ: 0.71; CI: 0.14, 1.3), more first antenatal care visits (aβ: 3.3; CI: 0.43, 6.2), and fewer clinic beds (aβ: -0.94; CI: −1.7, −0.20) showed more improvements. Compared to facilities with no stock-outs, facilities with five essential drugs stocked out had 51.7% (CI: −64.8 -38.6) lower data concordance.ConclusionsA data quality intervention was associated with significant improvements in health information system data concordance across public-sector health facilities in rural and urban Mozambique. Concordance was higher at those facilities with more human resources for health and was associated with fewer clinic-level stock-outs of essential medicines. Increased investments should be made in data audit and feedback activities alongside targeted efforts to improve HIS data in low- and middle-income countries.

Institutionalized Data Quality Assessments: A Critical Pathway to Improving the Accuracy of Integrated Disease Surveillance Data in Sierra Leone

“…Improvement in data accuracy observed from these assessments implies that the regular data quality assessments may have contributed positively to data accuracy and is consistent with ndings from repeated data quality audits conducted elsewhere [17][18][19]. Other contributors to improved data accuracy could be regular supportive supervision and shift to electronic reporting that were introduced simultaneously with the DQA.…”

Using Routine Health Information Data for Research in Low- and Middle-Income Countries: A Systematic Review

“…Many of the studies utilized the highly disaggregated nature of the data by using either facility or district level data, with the exception of two studies which modelled national trends 33,74 . Studies commonly applied strategies to account for temporal autocorrelation and the correlation between geographical units, including generalized linear models 58 , multi-level analysis 75,76 , and ordinary least-squares regression with adjustment for seasonality and lag 34,37,77 . Among studies that modelled multiple facilities or administrative regions, random effects were commonly applied to account for heterogeneity.…”

“…Exclusion of missing data was the most common practice, and among studies that used this technique, they excluded facilities from the analytic samples 38,41,45,52,65,91,[93][94][95][96][97][98] , restricted the study period based on explicit criteria 54,99 , or applied sensitivity analysis to compare various exclusion criteria 41,100,101 . Imputation methods varied from assigning speci c values to the missing observation 42,86,91,[102][103][104] , to various modeling strategies such as conditional autoregressive model 87 , generalized linear regression 103 , and iterative singular value decomposition 103 . A sensitivity analysis was also conducted to select a speci c imputation strategy 103 .…”

Donor data vacuuming

“…4 Over five years, semiannual meetings were held in each district during which district and facility managers spent three days reviewing and explaining their data to one another. With this simple but targeted audit and feedback intervention, data reliability improved dramatically from 54 to 87 percent overall (see figure 2) (Wagenaar et al 2015).…”