Data quality assessment in the routine health information system: an application of the Lot Quality Assurance Sampling in Benin

Ahanhanzo, Yolaine Glèlè; Ouendo, Edgard‐Marius; Kpozéhouen, Alphonse; Lévêque, Alain; Makoutodé, Michel; Dramaix-Wilmet, Michèle

doi:10.1093/heapol/czu067

Cited by 45 publications

(46 citation statements)

References 17 publications

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“…The low accuracy noted in this study corroborates the earlier findings of Glèlè et al [12] in Benin. Using LQAS method and a 5% threshold, they observed a discrepancy of over 25% of maternity and immunization data.…”

Section: Accuracy Of Data Collected For Snigssupporting

confidence: 92%

“…This improvement is significant compared to the findings of Glèlè et al [12]. Completeness level in the RBF strata is similar to that seen in Rwanda where the RBF has already been scaled nationwide [18].…”

Section: Quality Of the Data Collection Processsupporting

confidence: 79%

“…The poor quality of statistical data was also raised in Benin [11,12] where RBF was established in 2012. The RBF implementation requires that data be verified before they are used for incentives payment to health facilities.…”

Section: Introductionmentioning

confidence: 99%

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Effects of Results Based Financing Models on Data Quality Improvement in Benin on 2014

Salami¹,

Ouendo²,

Fayomi³

2016

ujph

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The performances generated by results based financing (RBF) raise questions about the quality of data used to measure them. This study was carried out to evaluate the RBF contribution to the quality improvement of maternal and child health data. Sixty seven health facilities are sampled randomly in three strata (RBF_PRPSS, RBF_PASS, No_ RBF) gathering health zones in Benin. The strata comparison with maternal and child health data for the first halves 2014 and 2011 revealed that timeliness and completeness have improved in the strata exposed to results based financing compared to unexposed (p <0.05). Compared to No_RBF stratum, accuracy deteriorated in RBF_PRPSS stratum, with a 52% decrease in the proportion of concordance reports between 2011 and 2014. In 2014, recorded discrepancies are largely under-reported in the RBF_PRPSS stratum, and over-reported in RBF_PASS stratum. The accuracy of National Health Information and Management System (SNIGS) data compared to RBF's validated data was low, verification factors varying between 30% and 97%. In total, the data quality was globally poor in most areas in Benin and it is not improved by the results based financing models implemented between 2011 and 2014. There is a need to target data quality in RBF models and to use SNIGS as a unique system of reporting.

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Section: Accuracy Of Data Collected For Snigssupporting

confidence: 92%

Section: Quality Of the Data Collection Processsupporting

confidence: 79%

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Effects of Results Based Financing Models on Data Quality Improvement in Benin on 2014

Salami¹,

Ouendo²,

Fayomi³

2016

ujph

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“…The poor quality of the HMIS data and underperformance found in this study and other studies in SSA is likely to be attributed to the combination of multiple factors. These include insu cient staff with core competence on data management, low motivation and lack of incentives, poor infrastructure, inadequate resources to conduct comprehensive supportive supervision and, lack of standard operating procedures [3,6,27,29]. Similar challenges were reported in an assessment carried out in 2015/2016 in Tanzania [22].…”

Section: Discussionmentioning

confidence: 84%

“…Variations and inadequate utilization of HMIS tools by facility characteristics have been reported previously in Tanzania and elsewhere [11,21,24]. Private-owned facilities, hospitals and healthcare facilities with high client volume are known to signi cantly affect the quality of HMIS data due to poor adherence in recording procedures, incompleteness and late reporting [3,5,6,9,13,[25][26][27]. The performance of urban districts on HMIS utilization and data quality has been reported by other studies with inconclusive results [12,15,26].…”

Section: Discussionmentioning

confidence: 89%

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

Rumisha

Lyimo

Mremi

et al. 2020

Preprint

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Background Effective planning for disease prevention and control requires accurate, adequately-analysed, interpreted and communicated data. This study assessed the quality of routine Health Management Information System (HMIS) data at healthcare facility (HF) and district levels in Tanzania. Methods HMIS tools used at primary health care facilities (dispensary, health centre, hospital) and district office were reviewed to assess their availability, completeness, and accuracy of collected data. The assessment involved seven health service areas namely, Outpatient department, Inpatient department, Antenatal care, Family Planning, Post-natal care, Labour and Delivery and Provider-initiated Testing and Counselling. Results A total of 115 HFs in 11 districts were assessed. Registers (availability rate = 91.1%; interquartile range (IQR):66.7%-100%) and report forms (86.9%; IQR:62.2%-100%) were the most utilized tools. There was a limited use of tally-sheets (77.8%; IQR:35.6%-100%). Tools availability at dispensary was 91.1%, health-centre 82.2% and hospital 77.8%, and was poor in urban districts. The availability rate at the district level was 65% (IQR:48%-75%). Reports were highly over-represented in comparison to registers’ records, with large differences observed at HF phase of the data journey and more profound in hospitals. Tool availability and data quality varied by service-areas, indicators, facility level, and districts, however, with a remarkable improvement over the years. Conclusion There are high variations and improvements in the tool utilisation and data accuracy at facility and district levels. The routine HMIS is weak and data at district level inaccurately reflects what is available at the HFs. These results highlight the need to design tailored and inter-service strategies for improving data quality.

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DAHS: A Distributed Data-as-a-Service Framework for Data Analytics in Healthcare

Boonma

Natwichai

Khwanngern

et al. 2018

Advances on P2P, Parallel, Grid, Cloud and Internet Computing

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Data quality assessment in the routine health information system: an application of the Lot Quality Assurance Sampling in Benin

Cited by 45 publications

References 17 publications

Effects of Results Based Financing Models on Data Quality Improvement in Benin on 2014

Effects of Results Based Financing Models on Data Quality Improvement in Benin on 2014

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

DAHS: A Distributed Data-as-a-Service Framework for Data Analytics in Healthcare

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