Optimizing data quality of pharmaceutical information systems in public health care in resource limited settings

H. R. Kagoya, D. Kibuule, T. W. Rennie, C. Wuletaw, K. H. Mitonga

Research output: Contribution to journalArticlepeer-review

4 Citations (SciVal)


Background: Robust pharmaceutical management information systems (PMIS) strengthen healthcare planning and delivery. Few nationwide studies in resource limited settings in Africa validate the data quality of PMIS in public healthcare. Objective: To determine predictors and quality of data in a nationwide PMIS database in Namibia. Methods: A population-level analysis of the quality of data i.e. completeness, accuracy and consistency in a nationwide PMIS database, 2007–2015. Data quality of the PMIS was determined by three domains, completeness, accuracy and consistency. Data completeness was determined by level of missing data in SPSSv25, with acceptable level set at <5%. Data accuracy was determined by proportion of PMIS indicators with extreme outliers. Data consistence was determined by patterns of missingness, i.e. random or systematic. Predictors of data quality were determined using logistic regression modelling. Results: A total of 544 entries and 12 indicators were registered in the PMIS at 38 public health facilities. All the PMIS indicators had missing data and 50% (n = 6) had inaccurate data i.e. extreme values. The data for most PMIS indicators (75%, n = 12) were consistent with the pattern of missing completely at random (MCAR, i.e. missingness <5%). Incompleteness of PMIS data was highest for average number of prescriptions 6%, annual expenditure per capita for pharmaceuticals 5% and population per pharmacist's assistant 5%. The main predictors of poor quality of PMIS data were year of reporting of PMIS data (p = 0.035), level of health facility (p < 0.001), vital reference materials available at the pharmacy (p = 0.002), and pharmacists' posts filled (p = 0.013). Conclusions: The data quality of PMIS in public health care in Namibia is sub-optimal and widely varies by reporting period, level of health facility and region. The integration of data quality assurance systems is required to strengthen quality of PMIS data to optimize quality of PMIS data in public health care.

Original languageEnglish
Pages (from-to)828-835
Number of pages8
JournalResearch in Social and Administrative Pharmacy
Issue number6
Early online date13 Sept 2019
Publication statusPublished - 30 Jun 2020

Bibliographical note



  • HEALTH information SYSTEMS
  • Pharmaceutical

ASJC Scopus subject areas

  • Pharmacy
  • Pharmaceutical Science


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