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Article: Addressing missing values in routine health information system data: an evaluation of imputation methods using data from the Democratic Republic of the Congo during the COVID-19 pandemic

TitleAddressing missing values in routine health information system data: an evaluation of imputation methods using data from the Democratic Republic of the Congo during the COVID-19 pandemic
Authors
KeywordsMissing data
Routine health information systems (RHIS)
Health management information system (HMIS)
Health services research
Low- and middle-income countries (LMICs)
Multiple imputation
Issue Date2021
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.pophealthmetrics.com/start.asp
Citation
Population Health Metrics, 2021, v. 19, article no. 44 How to Cite?
AbstractBackground: Poor data quality is limiting the use of data sourced from routine health information systems (RHIS), especially in low- and middle-income countries. An important component of this data quality issue comes from missing values, where health facilities, for a variety of reasons, fail to report to the central system. Methods: Using data from the health management information system in the Democratic Republic of the Congo and the advent of COVID-19 pandemic as an illustrative case study, we implemented seven commonly used imputation methods and evaluated their performance in terms of minimizing bias in imputed values and parameter estimates generated through subsequent analytical techniques, namely segmented regression, which is widely used in interrupted time series studies, and pre–post-comparisons through paired Wilcoxon rank-sum tests. We also examined the performance of these imputation methods under different missing mechanisms and tested their stability to changes in the data. Results: For regression analyses, there were no substantial differences found in the coefficient estimates generated from all methods except mean imputation and exclusion and interpolation when the data contained less than 20% missing values. However, as the missing proportion grew, k-NN started to produce biased estimates. Machine learning algorithms, i.e. missForest and k-NN, were also found to lack robustness to small changes in the data or consecutive missingness. On the other hand, multiple imputation methods generated the overall most unbiased estimates and were the most robust to all changes in data. They also produced smaller standard errors than single imputations. For pre–post-comparisons, all methods produced p values less than 0.01, regardless of the amount of missingness introduced, suggesting low sensitivity of Wilcoxon rank-sum tests to the imputation method used. Conclusions: We recommend the use of multiple imputation in addressing missing values in RHIS datasets and appropriate handling of data structure to minimize imputation standard errors. In cases where necessary computing resources are unavailable for multiple imputation, one may consider seasonal decomposition as the next best method. Mean imputation and exclusion and interpolation, however, always produced biased and misleading results in the subsequent analyses, and thus, their use in the handling of missing values should be discouraged.
Persistent Identifierhttp://hdl.handle.net/10722/308500
ISSN
2023 Impact Factor: 3.2
2023 SCImago Journal Rankings: 1.646
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorFeng, S-
dc.contributor.authorHategeka, C-
dc.contributor.authorGrépin, KA-
dc.date.accessioned2021-12-01T07:54:10Z-
dc.date.available2021-12-01T07:54:10Z-
dc.date.issued2021-
dc.identifier.citationPopulation Health Metrics, 2021, v. 19, article no. 44-
dc.identifier.issn1478-7954-
dc.identifier.urihttp://hdl.handle.net/10722/308500-
dc.description.abstractBackground: Poor data quality is limiting the use of data sourced from routine health information systems (RHIS), especially in low- and middle-income countries. An important component of this data quality issue comes from missing values, where health facilities, for a variety of reasons, fail to report to the central system. Methods: Using data from the health management information system in the Democratic Republic of the Congo and the advent of COVID-19 pandemic as an illustrative case study, we implemented seven commonly used imputation methods and evaluated their performance in terms of minimizing bias in imputed values and parameter estimates generated through subsequent analytical techniques, namely segmented regression, which is widely used in interrupted time series studies, and pre–post-comparisons through paired Wilcoxon rank-sum tests. We also examined the performance of these imputation methods under different missing mechanisms and tested their stability to changes in the data. Results: For regression analyses, there were no substantial differences found in the coefficient estimates generated from all methods except mean imputation and exclusion and interpolation when the data contained less than 20% missing values. However, as the missing proportion grew, k-NN started to produce biased estimates. Machine learning algorithms, i.e. missForest and k-NN, were also found to lack robustness to small changes in the data or consecutive missingness. On the other hand, multiple imputation methods generated the overall most unbiased estimates and were the most robust to all changes in data. They also produced smaller standard errors than single imputations. For pre–post-comparisons, all methods produced p values less than 0.01, regardless of the amount of missingness introduced, suggesting low sensitivity of Wilcoxon rank-sum tests to the imputation method used. Conclusions: We recommend the use of multiple imputation in addressing missing values in RHIS datasets and appropriate handling of data structure to minimize imputation standard errors. In cases where necessary computing resources are unavailable for multiple imputation, one may consider seasonal decomposition as the next best method. Mean imputation and exclusion and interpolation, however, always produced biased and misleading results in the subsequent analyses, and thus, their use in the handling of missing values should be discouraged.-
dc.languageeng-
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.pophealthmetrics.com/start.asp-
dc.relation.ispartofPopulation Health Metrics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectMissing data-
dc.subjectRoutine health information systems (RHIS)-
dc.subjectHealth management information system (HMIS)-
dc.subjectHealth services research-
dc.subjectLow- and middle-income countries (LMICs)-
dc.subjectMultiple imputation-
dc.titleAddressing missing values in routine health information system data: an evaluation of imputation methods using data from the Democratic Republic of the Congo during the COVID-19 pandemic-
dc.typeArticle-
dc.identifier.emailGrépin, KA: kgrepin@hku.hk-
dc.identifier.authorityGrépin, KA=rp02646-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s12963-021-00274-z-
dc.identifier.pmid34736462-
dc.identifier.pmcidPMC8567342-
dc.identifier.scopuseid_2-s2.0-85118762649-
dc.identifier.hkuros330688-
dc.identifier.volume19-
dc.identifier.spagearticle no. 44-
dc.identifier.epagearticle no. 44-
dc.identifier.isiWOS:000714571700001-
dc.publisher.placeUnited Kingdom-

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